Computer Science

Research area: My specialized research area is affective data science: building machine-learning and LLM-driven systems that can reliably read emotion from face and voice. I develop multimodal models that convert raw behavioral signals - facial motion, vocal acoustics, and related measures - into affect estimates. I also build reproducible pipelines, open tools, and benchmark studies that pit models against human judgments, probing not only accuracy but confusability structure, calibration, and bias. The goal is emotion AI that is both powerful and trustworthy.

Student roles:
The student will lead the technical implementation and day-to-day execution of the project. First, they will review and contrast available multimodal LLM/MLLM options for emotion classification from audiovisual stimuli, with attention to input modality support (video frames vs native video), local versus cloud deployment, licensing, reproducibility, and expected performance (e.g., Qwen3-VL and comparable open models). Based on this review, the student will select and justify an initial model set and define standardized inference conditions (frame sampling policy, resolution constraints, and label set).

Second, the student will build the experimental pipeline using an agent-assisted development workflow. This includes creating and maintaining a version-controlled Git repository, implementing video preprocessing (decode, downsample, frame extraction), orchestrating model inference (via Ollama and/or other runtimes), and enforcing strict prompt templates and output formats (e.g., forced-choice labels with JSON outputs). The student will iteratively refine prompt wording and decoding settings to ensure stability, minimize invalid outputs, and align model responses with the human rating format, while documenting all settings for reproducibility.

Third, the student will wrangle and collate outputs at scale: batch processing of RAVDESS clips, robust error handling, logging of runtime and configuration metadata, and generation of tidy datasets suitable for analysis. They will conduct exploratory statistical analyses comparing human versus model performance, including confusion matrices, agreement metrics, calibration, and mixed-effects models where appropriate. Results will be summarized in clear figures and tables.

Finally, the student will contribute to project documentation and dissemination, including technical documentation of the pipeline, experimental protocols, and draft text for reports or manuscripts. The student will meet weekly with the supervisor to review progress, troubleshoot issues, and plan next steps. They must be able to work independently, incorporate feedback quickly, ask well-formed questions when blocked, and communicate technical decisions and findings clearly in both written and verbal form.

Skills required:
The student should have strong programming ability (Python preferred) and experience with data science workflows for cleaning, automation, and reproducible analysis. They should understand modern LLM/MLLM practice, including prompting, structured outputs, and running local models (e.g., via Ollama), as well as machine learning evaluation (metrics, error analysis, calibration). Experience with agent-based development is helpful: using coding agents to scaffold pipelines, run tests, and manage iterative debugging under version control. Audiovisual processing skills are an asset (video decoding, frame sampling, resizing, optional audio feature extraction). The project also benefits from basic psychology knowledge, including emotion constructs and validity considerations.

Research area: Dr. Travis Esau specializes in precision and digital agriculture, focusing on the design and development of mechanized and autonomous systems to enhance farm productivity, sustainability, and profitability. His research integrates machine vision, artificial intelligence, and geospatial technologies to enable data-driven decision-making in crop production systems such as wild blueberry and potato. He advances innovations in automated harvesting, precision spraying, and field monitoring, while also addressing farm traceability and cost-of-production analysis. Through applied, field-validated research, his work supports the transition toward Agriculture 4.0, improving efficiency, reducing environmental impact, and strengthening the resilience and competitiveness of modern agricultural systems.

Student roles:
The expected outcomes of this project include implemented and validated control algorithms for UGV path following and bin pickup operations, along with supporting software integrated into the existing platform. Deliverables will include documented code for control system implementation, performance evaluation results from both simulation and field testing, and technical documentation describing system design, controller structure, and observed performance. Additional outputs may include refined control strategies that improve operational reliability, as well as prototype demonstrations of autonomous path following and bin handling under field conditions. Internal reports summarizing development progress and experimental findings will also be produced, with potential contributions to academic publications or presentations where applicable.

Skills required:
The student should have a strong background in robotics, control systems, and mechatronics, with experience in system modeling and controller design. Familiarity with kinematics, dynamics, and path-following algorithms is essential. The student should be comfortable implementing classical and modern control strategies, integrating sensor feedback into closed-loop systems, and working with embedded platforms. Experience in software development, debugging, and system integration is required. Knowledge of actuator control and sequencing is an asset. The student should be capable of laboratory testing and open to limited field validation. Strong analytical, problem-solving, and communication skills are expected, along with consistent progress reporting and teamwork.

Research area: The Toronto Intelligent Systems Lab has extensive experience in 3D reconstruction, with potential integration to robotics applications. Our work aims to enable embodied agents to understand and interact with complex real-world environments by addressing challenging environmental conditions, advancing interactive reasoning capabilities, and improving understanding of the 3D structure of the world.

Student roles:
The student will serve as a core member of the research team, and lead the research project through all stages of the research process. This includes understanding, working with and extending existing codebases, developing novel models and methods, reviewing related literature and reproducing baselines, collaboratively formulating new research directions and benchmarks. The goal is to lead to a research paper for publication in a top-tier ML/CV conference like CVPR, ECCV, ICCV, NeurIPS, ICML, ICLR, etc.

Skills required:
1. Familiarity with general machine learning and deep learning concepts
2. Familiarity with deep learning frameworks like PyTorch
3. Experience with implementing algorithms for computer vision tasks
4. General knowledge in computer vision, like understanding 3D transformation, camera matrices, etc.

Research area: Older adults represent a significant and growing segment of the population, doubling from an anticipated 1.4 billion people (1 in 6) in the world aged 60 years in 2030 to 2.1 billion by 2050. They form a growing segment of users of emerging technologies such as conversational agents powered by artificial intelligence (AI). However, their unique needs are often ignored in the design and development of these new technologies. Our research aims to explore new ways of understanding and developing technology to ensure digital spaces remain accessible, inclusive, and relevant to aging populations.

Student roles:
The student’s primary task will be to design/develop a conversational AI application designed for use by older adult users. The student’s responsibilities are divided into the following three tasks:

1) Build the application. The student will conduct market research and literature review to determine a use case that could have valuable research and societal contributions. After settling on an idea with the supervisor, the student will implement a preliminary version of the application. This prototype can be simple (e.g., a soundboard on Powerpoint) or complex (e.g., with the Meta SDK), based on the students’ skills and project needs.

2) Iteratively refine and improve the application. This will start with internal evaluation with the project team before moving to evaluation with external stakeholders (e.g., adult users). The student will be in charge of conducting internal evaluation focusing on user experience metrics including usability, user perceptions, accessibility, and acceptance. The student will assist with any evaluation with external stakeholders.

3) Report Writing & Project Wrap Up. The student will report on their process and findings in a structured manner (e.g., in a report). The student will prepare project files (e.g., the application source code) in a manner that can be used by the project team for further research and development.

Skills required:
REQUIRED:
- Studying a discipline that can inform the design of the application.
- Excellent written and oral communication skills.
- Willingness to learn any programming languages and environments necessary to design/develop the application.

ASSETS:
- Prior experience working with relevant technologies (e.g., conversational AI, chatbots, voice applications).
- Prior experience/expertise with user experience research (e.g., interviews, surveys, data analysis, thematic analysis).
- Prior experience building products based on user requirements.
- Prior experience designing research studies (e.g., drafting research protocol, interview/survey questions, etc.)
- Demonstrated teamwork skills.
- Experience working with older adults.

Research area: I specialize in computer graphics, virtual humans, crowd simulation, and immersive virtual reality. My research focuses on building realistic and interactive digital environments where virtual characters can perceive, move, communicate, and respond to users in socially believable ways. This includes emotion-aware avatars, pedestrian and crowd behaviour, embodied interaction in VR, real-time animation, and simulation-based evaluation of human movement and social spacing. The broader goal of my work is to create intelligent virtual environments for games, film-making, training, digital twins, education, and human-centred simulation.

Student roles:
The student will help design, implement, and evaluate a prototype system for scene-aware conversational avatars. At the beginning of the internship, the student will review related work and examples on conversational agents, virtual humans, scene understanding, turn-taking, and nonverbal behaviour. Based on this review, the student will help define a small interaction scenario involving one human user and multiple virtual characters.

The student will implement a simple scene representation that stores information about important objects, character positions, user location, gaze direction, and nearby events. This scene information will be connected to a large language model so that the avatars can produce responses that are relevant to the current environment. The student will also help build a dialogue manager that controls who speaks, when they speak, and how the system handles turn-taking in a multi-party conversation.

Another important part of the project will be connecting speech with nonverbal behaviour. The student will integrate generated dialogue with text-to-speech output and simple avatar behaviours such as gaze shifts, head turns, facial expressions, pointing gestures, idle motion, and posture changes. The goal is to make the avatar’s verbal and nonverbal behaviour appear coordinated and socially believable.

The student will create demonstration scenarios, such as a virtual guide explaining objects in a room, a training assistant responding to user questions, or two virtual characters jointly interacting with a human user. The student will also assist with evaluation using measures such as response relevance, turn-taking quality, timing, user engagement, naturalness, social presence, and comfort.

By the end of the 12-week internship, the student is expected to deliver a working prototype, documented source code, demo videos, a short technical report, and initial evaluation results.

Skills required:
The student should have a background in computer science, game development, computer graphics, artificial intelligence, human-computer interaction, or a related area. Experience with Unity or Unreal Engine is highly desirable. Programming experience in C# / C++ (for Unity or Unreal) is expected. Familiarity with LLMs, APIs, dialogue systems, speech-to-text, text-to-speech, character animation, or VR development would be a high asset. The student should be interested in building interactive systems involving virtual humans, conversation, animation, games, or immersive environments.

Research area: My research lies at the intersection of corporate governance, auditing, judgment and decision-making, business analytics, and applied machine learning, with applications to sustainability and responsible organizational decision-making. I study how organizational actors, governance structures, and data-driven systems influence business decisions, accountability, and reporting quality. My current work examines the use of machine learning, large language models, and computational methods to study managerial judgment, auditor behaviour, corporate reporting, and responsible decision-making. Methodologically, my research combines experimental design, statistical modelling, simulation, machine learning, computational text analysis, causal inference, and longitudinal data analysis.

Student roles:
The student will contribute to a project that uses cutting-edge data analytics and deep learning models to study how relationships among auditors, senior executives, board directors, and public companies form, evolve, and terminate over time. The role will combine literature review, data preparation, computational analysis, visualization, documentation, and research reporting.
The student’s responsibilities will include:
1) Reviewing relevant research on AI and machine learning applications in business, auditing, corporate governance, executive and director relationships, and longitudinal data analysis.
2) Helping identify and organize key concepts from the literature to support the development of the project’s research design and empirical approach.
3) Cleaning and organizing longitudinal data on companies, auditors, executives, and directors, including checking data quality, consistency, missing values, and variable definitions.
4) Preparing structured datasets for analysis by creating appropriate identifiers, time-based observations, and variables that capture relationship formation, continuation, and termination.
5) Implementing exploratory data analysis and selected statistical or machine learning methods to identify meaningful patterns in complex corporate governance data.
6) Creating tables, charts, and visualizations to summarize descriptive patterns, model outputs, and key research findings.
7) Documenting code, data-cleaning decisions, and analytical procedures to support transparency and reproducibility.
8) Contributing to written summaries, presentation materials, and a final research report that communicates the project’s methods, findings, and implications.
Through this role, the student will gain hands-on experience with real-world business data, data cleaning, applied machine learning, data visualization, interdisciplinary research, and computational approaches to corporate governance and auditing. The student will also strengthen transferable skills in critical thinking, reproducible research, technical communication, and collaborative problem-solving.

Skills required:
The ideal student will have a background in computer science, data science, artificial intelligence, machine learning, engineering, or a related quantitative field. Strong programming skills in Python are required, especially experience with data cleaning, data structures, and libraries such as pandas, NumPy, scikit-learn, or similar tools. Experience with SQL, R, Stata, data visualization, natural language processing, or applied machine learning would be an asset. The student should be comfortable reading academic papers, working with messy real-world data, documenting code, and communicating results clearly. Curiosity about corporate governance, auditing, organizational relationships, and responsible use of AI/data analytics is especially welcome.

Research area: My research area is primarily centered on the methodological aspects (approaches, processes, methods and tools) for the development of complex products and systems (e.g. mechatronics, cyber-physical systems, smart products) in the context of business and societal transformation. In connection with the societal transformation, and as part of this project, my interest focuses on Quebec's healthcare innovation ecosystem. One of the main aspects of the project is to map the different types of stakeholders and exchange flows on a perennial basis.

Student roles:
The student's primary role will be to initiate the creation of the “shell” that will host the map. This essentially involves creating a database (possibly graph-oriented), with a particular focus on the back-end. The front-end can be developed according to the student's profile (UX/UI skills). The primary aim is to converge towards the creation of a first demonstrator. To structure the development, it will be possible to rely on Scrum, the creation of backlogs, user stories and two-week sprints. As mentioned above, a software architecture (UML) will also be ready to start the project.

The 12 weeks can be broken down as follows:
-Becoming familiar with the research project (2 weeks). This includes familiarization with the documentation and the work carried out, as well as a more detailed evaluation and planning of the work. A prioritization will have to be established.
-Programming of prioritized functions (8 weeks - 4 sprints), leading to a demonstrator of the map.
-Explanatory synthesis and exhaustive documentation of the work carried out, to facilitate project transfer and continuation (2 weeks).

The student's role is to produce a functional demonstrator to exemplify the final application for the scientific community and the Quebec healthcare innovation ecosystem. This work will serve as the starting point for the remaining stages of the project.

Skills required:
The ideal candidate is a student in software engineering/computer science who wants to apply his/her programming skills. A portfolio of previous projects attached to the application would be a great plus (e.g. GitHub). The student is expected to be rigorous, autonomous (one meeting per week), able to benchmark and evaluate “competing” solutions, have excellent synthesis skills, as well as being able to propose new ideas. The programming languages to be used have not yet been determined at this stage of the project, but will be determined over the coming months.

Research area: This interdisciplinary project brings together engineering, biomechanics, health sciences, psychology, and the creative arts to develop technology-enhanced approaches for promoting physical activity and social engagement among older adults. Researchers from engineering, kinesiology, psychology, and digital arts collaborate to develop wearable sensing technologies, motion capture systems, and movement-to-art tools that transform body movement into real-time music and visual art. The project integrates human movement analysis, wearable technology, qualitative and quantitative research methods, and community-engaged research to better understand how creative expression can motivate physical activity, improve quality of life, and support healthy aging.

Student roles:
The role of the student will be flexible and will depend on the current priorities of the research team, the stage of the project, and the student’s background, skills, and interests. The student will primarily support a larger interdisciplinary project focused on technology-enhanced art therapy for older adults, with the goal of using movement-based creative expression to promote physical activity, motivation, quality of life, and social connection.
During the internship, the student may assist with several aspects of the research lifecycle. These may include supporting data collection during participant assessments, such as walking, balance, functional mobility, and questionnaire-based assessments; helping organize and clean quantitative datasets; processing motion capture, wearable sensor, or camera-based movement data; assisting with usability or technology validation activities; reviewing relevant research literature; and contributing to qualitative research activities such as interviews, transcription, coding, or summary of participant feedback. Depending on their background, the student may also help support technology development, pilot testing, data visualization, signal processing, or knowledge mobilization activities.
The student will work closely with the supervisor, graduate students, postdoctoral researchers, and other members of the interdisciplinary team. They will participate in regular lab meetings and one-on-one supervisory meetings to discuss progress, training needs, timelines, and goals. The internship will include both independent tasks and collaborative work with small groups of trainees and faculty members.
Training will be provided in all required research methods, ethics procedures, data management practices, participant interaction protocols, and technical tools. The successful student will be encouraged to take an active role in identifying achievable goals for the 12-week internship and, where appropriate, may contribute to conference presentations, reports, publications, or other dissemination activities. Overall, the student will gain hands-on experience in interdisciplinary, community-engaged research involving older adults, wearable technology, motion analysis, and mixed-methods evaluation.

Skills required:
The successful student should be motivated, organized, and capable of working both independently and as part of a collaborative research team. Due to the project's diverse topics, backgrounds in engineering, computer science, kinesiology, health sciences, rehabilitation sciences, psychology, or related disciplines are welcome. Experience with data analysis, programming, motion capture, wearable sensors, biomechanics, qualitative research, or working with human participants would be beneficial but is not required. Strong communication skills, curiosity, professionalism, and a willingness to learn new research methods are essential. Training will be provided and responsibilities will be tailored to the student's background, interests, and project goals.

Research area: Human-Centered Innovation Research (HCIR) investigating how technological development, guided by artistic sensibilities and ethical reflection, can meaningfully enhance human well-being. My research examines international business strategy in digital platform environments, focusing on how entrepreneurs navigate cross-cultural markets through technology-mediated channels. Current focus explores how digital platform entrepreneurship enables market entry and expansion across cultural boundaries, with particular emphasis on creative industry ventures and content-based businesses. This interdisciplinary approach bridges international business strategy, digital entrepreneurship, and platform economics to understand how technological mediations transform cross-border business development.

Student roles:
Business Literature Analysis: Conduct systematic literature reviews of international entrepreneurship, platform business models, and cross-cultural market entry strategies. Students will analyze theoretical developments in digital internationalization, identify research gaps, and synthesize findings to inform empirical research design focused on platform-mediated business expansion.
Platform Business Model Documentation: Systematically document platform monetization systems, business policies, and international expansion mechanisms. Students will analyze how different platforms structure revenue sharing, audience development tools, and cross-border payment systems, examining how these systems enable or constrain international business development for digital entrepreneurs.
Entrepreneur Strategy and Performance Analysis: Assist with collecting business performance data from digital entrepreneurs across different cultural markets. Students will help develop and apply frameworks for analyzing market positioning strategies, revenue models, and audience development approaches, documenting how entrepreneurs adapt their business strategies for international success.
Cross-Cultural Market Analysis: Support analysis of how digital entrepreneurs navigate different cultural markets and regulatory environments. This includes examining pricing strategies, content localization approaches, and audience engagement tactics across diverse international contexts, with attention to cultural factors affecting business performance and market penetration.

Skills required:
Ideal candidates bring interdisciplinary backgrounds combining business studies with digital media experience. Valuable backgrounds include international business, entrepreneurship, marketing, digital commerce, or related fields with experience in business analysis, market research, or digital business strategies. Experience with social media platforms, content monetization, or digital entrepreneurship is highly beneficial. Students with backgrounds in both business research methods and practical digital business knowledge are particularly encouraged. Strong analytical skills, cross-cultural awareness, and ability to work with diverse business data are essential. Interest in international business, digital entrepreneurship, or platform economics is important. Multilingual capabilities valuable for analyzing business strategies across different markets.

Research area: My work looks at what happens to nuclear power plant operators when AI systems take on more of the monitoring and decision-support work. The core problem, drawn from three foundational papers (Parasuraman & Riley, 1997; Endsley, 1995; Lee & See, 2004), is a feedback loop: operators who overtrust AI disengage from active monitoring, their diagnostic skills atrophy, and because their awareness has degraded they can no longer catch AI errors, so the miscalibration never corrects. My empirical work aims to use a virtual NPP environment to measure how operator behavior and attitudes shift at different automation levels.

Student roles:
The student will work with a team of other undergraduate and graduate students in carrying out the required tasks. The working hours are 40 per week. The student will also need to conduct a literature survey and summarize the status quo of other methods through a thorough comparative analysis.
Finally, the student will summarize their findings, analyze their results, and prepare tables, graphs, or other documentation to present their research. The research progress is expected to be presented during group meetings.

Skills required:
Task 1: The intern will search these literatures, extract findings on trust, situation awareness, and deskilling, and build a structured comparison table with notes on where nuclear’s specific conditions such as long monitoring shifts, catastrophic consequences and strict regulatory accountability make the analogy weaker.

Task 2: Measurement Instrument Review. The intern will review the options, recommend a battery, and ideally run an informal pilot with lab members before the instruments go near actual experimental participants.
Preferred qualifications: Background in engineering, psychology, or cognitive science. Comfortable with academic literature.

Research area: Prof. David Michelson is director of the UBC Radio Science Lab, lead faculty advisor to the UBC Orbit Satellite Design Team, and principal investigator of the ALEASAT CubeSat project sponsored by the European Space Agency's Fly Your Satellite program and the Trailblazer CubeSat project sponsored by SenseNet. His specific research interests include characterization, design, and implementation of Earth-space communications links, satellite-based remote sensing, and development of functional test facilities for small satellites.

Student roles:
Following an initial briefing and familiarization session, the student and supervisor will mutually agree on one or more sub-projects that the student will pursue as a contribution to the ALEASAT and/or Trailblazer projects. The sub-projects may involve: 1) aspects of design, implementation, and test of a 1U or 6U CubeSat, 2) design, implementation, and verification of a satellite Earth station for use with the CubeSats, 3) design, implementation and verification of functional test facilities used to verify the CubeSat, and/or 4) design, implementation, and verification of mission control software for use with the CubeSats,. The student will collaborate with existing UBC Orbit team members during the course of the term.

Skills required:
The student should be interested in pursing a career in wireless telecommunications and/or the space industry and contributing to high quality work that will be suitable for conference presentation or journal publication. We are willing to host two types of students: 1) students who are already involved with a CubeSat project at their home institution and want to participate in an exchange of skills, knowledge, and experience with their counterparts at UBC or 2) students who are interested in initiating a CubeSat project at their home institution and want the benefit of experiencing and contributing to an existing project.

Research area: My specialized research area lies at the intersection of machine learning, deep learning, and natural language processing (NLP), with a focus on multilingual models and large language models (LLMs). I am particularly interested in advancing methods for trustworthy, multimodal AI systems.

Student roles:
The student will support the development and evaluation of techniques to reduce cultural hallucinations in large language models across multiple languages. Responsibilities include implementing model-agnostic methods, testing on benchmark datasets, assisting with deployment and documentation, and contributing to the design and evaluation of user-facing tools that help detect and manage cultural hallucinations.

Skills required:
The ideal student has strong programming skills, particularly in Python, and a solid understanding of machine learning algorithms and data structures. Experience in natural language processing (NLP) or related fields is preferred. Familiarity with transformer architectures and large language models is important. Interest or experience in trustworthy AI, multimodal learning, explainable AI, or Human-Computer Interaction (HCI) is a plus. Strong analytical and problem-solving skills are essential.

Research area: artificial intelligence, machine learning, deep learning, machine translation, text mining, natural language processing

Student roles:
- contribution to research projects
- contribution to development of open-source prototypes, and to the evaluation of implemented systems
- discovering state-of-the-art literature for the research domains
- sharing ideas and acquired knowledge with team members
- contribution to the writing of scientific papers could be discussed

Skills required:
- good programming skills
- Linux OS
- autonomous
- able to work in a team
- interested in sentiment analysis, information retrieval, machine learning, speech recognition
- interested in multidisciplinary research

Research area: Our work focuses on intelligent mobile sensing and geospatial analytics for urban curbside management. We integrate vehicle-mounted high-resolution cameras, RGB-D/depth sensors, IMUs, and RTK-GNSS with computer vision, deep learning, and GIS to detect, classify, and map curbside parking activity and infrastructure constraints. Current work emphasizes automated extraction of parking occupancy, curb access features, signage, driveways, alleys, hydrants, and other roadside assets, supported by spatial data fusion and AI/LLM-based verification to produce reliable, GIS-ready inventories for equitable and efficient curbside planning.

Student roles:
The student will support selected components of the curbside management research project based on their background, their skills, and project needs. The role may include assisting with data preparation, annotation, computer vision model development, spatial analysis, and documentation for automated curbside inventory and parking-utilization assessment.

Specific tasks may involve organizing image, video, GPS/GNSS, IMU, and GIS datasets; annotating parked vehicles and curbside features such as signs, driveways, alleys, hydrants, loading areas, and access points; and helping train, test, or evaluate object detection, segmentation, tracking, OCR, or vision-language models. The student may also contribute to quality control by reviewing automated outputs, identifying detection errors, checking data completeness, and supporting human-verification workflows.

Depending on their expertise, the student may assist with GIS-based mapping of curbside features, linking detections to roadway segments, estimating effective curb length and parking capacity, or preparing inputs for simulation and scenario testing. These analyses may help evaluate how alternative curbside strategies affect parking availability, traffic performance, safety, accessibility, and equity.

An average day will involve working at a computer in the research lab, processing datasets, running scripts, reviewing model outputs, preparing maps or summaries, and documenting workflows and results. The student will participate in regular meetings with the research team to discuss progress, technical challenges, and next steps. The final role will be scoped to ensure it is achievable within the internship period while contributing meaningfully to the broader project objectives.

Skills required:
• The student should have programming experience in Python and an understanding of data analysis, machine learning, and deep learning. Familiarity with computer vision, object detection, classification, and tracking, image/video processing, or street-level imagery analysis is highly desirable.
• Experience with GIS software such as ArcGIS or QGIS, spatial data processing, or remote sensing is an asset.
• Knowledge of transportation systems, parking analytics, traffic operations, simulation, or equity analysis would be beneficial.
• Strong documentation, problem-solving, and communication skills are required, along with motivation to work in an interdisciplinary research environment combining AI, urban mobility, and infrastructure planning.

Research area: Our research focuses on multi-robot systems and swarm robotics. We follow a full stack research approach: perception (mapping, reasoning, terrain analysis...), navigation (path and mission planning), communication (ad-hoc networks, delay tolerance...), swarm intelligence, interfaces, operations (energy maintenance, fault tolerance), and machine learning.

Student roles:
- Work with multi-robot systems or swarms of terrestrial or aerial robots
- Investigate current (unsolved) Machine Learning and AI problems
- Implement and compare a variety of algorithms from current research
- Possibly help with the execution of a HRI user study
- If successful: Contribute to a journal or conference research paper

Skills required:
Programming skills are necessary. Experience with Machine Learning, AI, Computer Vision and/or Robotics is desirable.

Research area: Cybersecurity of EV charging infrastructure, threat modeling, network intrusion detection, vulnerability analysis, and attack simulation.

Student roles:
Weeks 1–2: Threat Landscape Review
• Survey published vulnerabilities in ISO 15118, OCPP, and EV charging security literature
• Document the attack surface of the Secure EV testbed architecture
• Develop STRIDE-based threat model
Weeks 3–6: Attack Implementation (Virtual Environment)
• Set up virtual testbed mirror
• Implement and document: replay attack, man-in-the-middle, DoS, OCPP command injection
• Capture and analyse malicious traffic signatures
Weeks 7–10: Live Testbed Security Testing
• Transfer attack scenarios to real Secure EV hardware (supervised)
• Deploy and validate Snort/Suricata IDS rules for EV charging traffic
• Test mitigation strategies: TLS 1.3 enforcement, certificate pinning, rate limiting
Weeks 11–12: Reporting & Disclosure
• Prepare responsible vulnerability disclosure report for industry partners
• Document IDS rule set and mitigation playbook (open research artifact)
• Contribute to joint research publication

Skills required:
Academic Background: Computer Science, Cybersecurity, Computer Engineering, or related field. Network security fundamentals, Python or Bash scripting, Wireshark or similar traffic analysis

Research area: My research is mainly in the field of cybersecurity. This area is central and very critical in the new era of digital transformation that we are experiencing. Indeed, the increased connectivity and exposure to digital technology increases the risks and threats of cyberattacks that can have both human and material impacts. In this context, my research aims to develop innovative solutions for the detection, analysis, mitigation, and prevention of emerging cyber threats using artificial intelligence and data science technologies. My research interests include malware detection and analysis, cyber-threat intelligence, secure software engineering, and social media security.

Student roles:
The student will participate in a research project, which involves the following tasks:
• Collect data from publicly available datasets of cyber threats.
• Pre-processing and analysis of the collected data.
• Extract relevant features from the collected data.
• Design and implement a prototype for detecting anomalies using artificial intelligence models.
• Conduct experiments to validate the proposed framework.
• Disseminate the research results in high-quality research papers and reports.

Skills required:
The student must be enrolled in Computer science/Computer engineering or related fields. Interested students must have a solid academic background with excellent programming skills. In addition, the student must demonstrate strong interest and motivation to conduct high-quality research in the cybersecurity field. Interested students should also possess strong analytical, problem solving and communications skills (French or English). Proven experience in cybersecurity and/or machine learning models is a plus.

Research area: Our laboratory bridges the gap between academic innovation and real-world adoption across trustworthy technology, security operations, and human readiness. On the human dimension, we study how to develop the skills, awareness, and judgment required to secure digital environments. Our work spans workforce preparation, curriculum development, and structured learning approaches. We investigate how to replace passive, compliance-based education with active, challenge-based frameworks. The objective is to design modular, empirical training platforms that drastically reduce time-to-competence for new practitioners, improve real-world risk perception, and operate independently of continuous laboratory oversight.

Student roles:
The selected intern will act as a Full-Stack Developer and Educational Tools Researcher, taking full ownership of building and validating our cybersecurity training platform. Over the 12-week internship, the student will lead a rapid software development lifecycle:

Initially, the student will map out the system architecture and design the user journeys for a series of interactive, threat-based learning scenarios. They will then write the code for both the frontend interactive interface and the backend logic that drives the simulation states. A critical technical milestone involves engineering the data collection pipeline, which captures and normalizes live user telemetry, such as response delays, incorrect answers, and path choices.

The student will implement the analytical engine that processes these telemetry logs to calculate localized proficiency metrics and generate dynamic progress dashboards for instructors. To validate the platform's efficacy, the intern will run a small pilot evaluation within the lab, verifying that the system accurately measures skill progression and successfully differentiates between novice and advanced user behaviors.

In line with our lab’s emphasis on research entrepreneurship, the student will treat the codebase as a viable, independent software asset. They will containerize the application using Docker, establish clean API frameworks, and author comprehensive administrator setup guides. This ensures that external educators can host and expand the platform without continuous assistance from our research team. The student will actively share technical updates during weekly lab syncs.

Skills required:
Applicants should be pursuing a degree in Computer Science, Software Engineering, Information Systems, or Human-Computer Interaction (HCI). Strong proficiency in software development and database management is required. Familiarity with gamification principles, instructional design, or user telemetry tracking is highly beneficial. Candidates must possess a solid understanding of basic cybersecurity concepts and a passion for creating clean, user-centric interfaces. We seek organized, self-motivated students who want to build a polished, customer-ready educational tool that bridges the gap between technical training and human performance evaluation.

Research area: Dr. Hossein Rouhani is an Professor in the departments of Mechanical Engineering and Biomedical Engineering, at the University of Alberta. His research areas are human motion measurement, musculoskeletal biomechanics, biomedical instrumentation design, biomedical signal processing, wearable technologies, orthopedic and rehabilitation engineering, gait and posture assessment, neural control systems. The application of Dr. Rouhani's research and technology development is in medical diagnosis devices, rehabilitative technologies, athletic performance assessment, work-related-injury prevention, and fall detection. Dr. Rouhani collaborates with a number of university hospitals across Canada within his interdisciplinary and translational research program.

Student roles:
The following tasks will be performed:
1) The student learns about principals of the sensors function and human motion biomechanics
2) The student will develop algorithms and computer programs to calculate target biomechanical parameters such as the ankle, knee, hip joint angles and moments during walking, standing up or sitting down.
3) The student will validate the accuracy of obtained parameters against in-lab measurement systems. The student will conduct the experimental study on the human subject and will process the data using the developed computer programs.
4) The student will develop software packages for the user of the developed technology.
5) The student will have the opportunity to interact with users (clinicians and patients) at a hospital. If so, the student will be asked to conduct human motion measurements on patients with movement disorders (such as stroke survivors), analyze the data, and compare the results of patients with able-bodied individuals.
6) The student must prepare the detailed report on the project at the end of the internship.

Skills required:
The student must be familiar with computer programming using MATLAB and engineering mechanics. Knowledge in mechatronics is an asset. Electrical, Biomedical, Mechanical, Computer Engineering students would typically have the required background. The student must be familiar or willing to learn data acquisition, physiological measurements, and data/signal processing, human motion biomechanics, and biomechatronics.

Research area: My research is in the general area of Human-Computer Interaction. In particular, my research is in the user modelling theme of HCI. My research involves developing applications and games to influence the attitude or behaviour of people for their good and that of society. In addition, my research consists in analysing user-generated data from social media to understand users' behaviour in order to predict future behaviour

Student roles:
The student will contribute to the design and execution of the research project. Responsibilities will include conducting a literature review on dark patterns, persuasive technologies, and trustworthy AI; assisting with the development of a benchmark dataset of prompts and AI-generated responses; collecting and organizing data from multiple AI systems; and helping to identify, categorize, and analyze manipulative interaction patterns. The student will also assist in developing a taxonomy of AI dark patterns, evaluating model behaviours across different scenarios, and preparing research reports and publications. Through these activities, the student will gain experience in AI evaluation, human-computer interaction, responsible AI research, data analysis, and scientific communication.

Skills required:
The student should have a background in Computer Science or a related field, with foundational knowledge of programming, data analysis, and artificial intelligence. Experience with Python, data collection, and software development is desirable. An interest in human-computer interaction, AI ethics, responsible AI, or user experience research would be beneficial. Strong analytical and problem-solving skills are important, as the project involves evaluating AI-generated responses and identifying patterns of potentially manipulative behaviour. The student should also possess effective communication skills and be willing to work independently while collaborating with researchers on data analysis, reporting, and dissemination activities.

Research area: I work on business process improvement, automation, and change management, with a focus on small companies. I am mostly interested in how technology integrates into work systems, including how it impacts people and work practices. Process mining is a data analytics technique that relies on event logs to reconstruct workflows and find patterns or predictions. Process mining, invented in the 1990s, is piercing into the private sector as process automation and orchestration emerge, especially with agentic automation becoming mainstream. I am starting a research program to examine how process data, automation, and analytics can support sustainable and intelligent decisions.

Student roles:
The student will work closely with the supervisor and graduate students in the lab. They will participate in all stages of the design science process, from problem exploration to prototype development, building and evaluation.
Initially, the student will help explore example event logs and process datasets, learning how standard process mining tools represent workflows and where these representations break down. Based on this exploration, the student will contribute to defining what “dark processes” look like from a data and algorithmic perspective (e.g., missing timestamps, unexplained waiting times, inconsistent traces).
The core technical task will be to help develop a Python-based prototype that analyzes event logs and produces diagnostic outputs, such as signals of low data completeness or visual markers of uncertainty in process flows. Depending on interests and skills, this may include basic algorithm design, data preprocessing, simple heuristics, or visualization components. The goal is not a production-ready system, but a clear, functional research artifact that demonstrates original thinking.
Beyond technical work, the student will be involved in documenting design choices and results, learning how computer science artifacts are explained and justified in research contexts. They will also participate in monthly lab meetings and weekly check-ins with the supervisor or a graduate student. The student will [HE1.1]prepare a poster or short demonstration describing the prototype and its insights at the end of the project.[HE2.1]
By the end of the project, the student will have tangible outputs: a working prototype, well-documented code[HE3.1], and experience explaining a research contribution, assets that are valuable for both graduate studies and industry roles in analytics, automation, or digital transformation. They will also have a draft of a short paper ready to be sent to an appropriate conference, for instance, the International Conference on Process Mining, whose paper submission deadline is usually in September.

Skills required:
The student should have strong English skills (reading, writing, speaking), as the project involves reading documentation, discussing design choices, and presenting results. Basic programming skills in Python are required, including comfort with data manipulation and simple scripts. Familiarity with process mining, business processes, or process automation is a bonus but not required, as training will be provided. Interest in digital transformation, data analytics, or socio technical systems is an asset. The project is suitable for students in computer science, management information systems, or related fields who want hands on research experience.

Research area: My research focuses on the development of advanced optimization and decision-support methods for large-scale industrial systems. In particular, I work at the intersection of operations research, artificial intelligence, and machine learning, with applications in supply chain management, retail operations, logistics, transportation, and resource planning. My research investigates how optimization, data analytics, and learning-based methods can be integrated to improve the quality and speed of decision-making under uncertainty. This includes mathematical optimization, decomposition methods, learning augmented optimization, predictive analytics, and intelligent algorithms for complex planning and scheduling problems arising in industry.

Student roles:
The student will join an active research team and contribute to ongoing projects led by PhD students and senior researchers. The primary role will be to support the development and evaluation of optimization and learning-based methods for retail, warehouse, and supply chain applications. This may involve implementing and testing new algorithms, conducting computational experiments, analyzing data, comparing alternative solution approaches, and evaluating performance across different problem instances.

The student will work closely with graduate students to understand existing models and software tools, reproduce and validate results, and help assess the effectiveness of newly proposed methods. Depending on the student's interests and skills, responsibilities may also include software development in Python or C++, visualization of results, and the preparation of technical reports and presentations.

This position offers an opportunity to gain handson research experience while contributing to cutting edge projects at the intersection of optimization, machine learning, and data analytics. The student will be expected to work independently on assigned tasks, collaborate effectively with team members, and actively participate in technical discussions and project meetings.

Skills required:
Candidates should have strong programming skills in Python or C++, good analytical and problem-solving abilities, and an interest in optimization, machine learning, artificial intelligence, and supply chain analytics. Familiarity with data structures, algorithms, linear algebra, probability, and mathematical optimization is desirable. Students who enjoy building practical solutions to real world problems are especially encouraged to apply.

Research area: My specialized research area in computer science relates to data management and data mining. In particular, my research team focuses on (a) data science, (b) data mining and analytics, (c) visualization and visual analytics, as well as (d) social network analysis and mining in recent years. Similar to the Mitacs Globalink Research Interns (GRIs) in summers 2013-2026, the student participates in this Globalink 2027 project will learn knowledge discovery and data mining techniques to provide a data science, visual analytics or business intelligence solution to visualize and analyze interesting data and knowledge. Recent research outcomes can be found in https://sites.google.com/site/dblabuofm/publications

Student roles:
Research within the Database and Data Mining Laboratory in Department of Computer Science at University of Manitoba mainly focuses on databases and data mining, which includes efficient and effective management of, knowledge discovery from, as well as analysis of, large amounts of data (such as transactional, uncertain, social media, Web, biomedical & healthcare data, and/or streams of data, as well as Big data). My current research programs focus on data mining as well as applications of data mining technologies to social computing and/or social network analysis. Our lab is a multi-cultural environment. Current and previous lab members include students from Asia (e.g., Bangladesh, China, India, Japan, Malaysia, South Korea), Central and South Americas (e.g., Brazil, Mexico, Paraguay), and Africa (e.g., Tunisia). Current and previous collaborators include researchers from Australia and France. Following our success in the Globalink 2013-2026 projects with GRIs (who have published their research findings from their Globalink research with me in refereed international conferences/journals & who are now graduate students in Canadian/US universities such as UBC, Waterloo, Columbia, CMU & UIUC or working in companies such as Google), the student participating in this Mitacs Globalink 2027 project will work under the same supervision model. In other words, the student will academic supervised by me (a Full Professor at the University of Manitoba) for an approximate 12-week research internship in the summer (with a flexible start date in May, June, or July). In addition, the student will also work closely with a senior lab member (who will serve as a mentor) and will contribute a small concrete visualization component of the project suitable at the level for the undergraduate students.

Skills required:
Similar to skill requirements for the Globalink 2013-2026 GRIs (now in top Canadian/US universities like UBC, Waterloo, Columbia, CMU & UIUC or companies like Google), students participating in this 2027 project are expected to have taken some third-year computer science courses (e.g., database courses) when they started the internship. Specific details of the project will be finalized after discussion and mutual agreement between the professor and student. In general, the student will be asked to design/implement in C/C++, Java, Python, R (or others), and may use Tableau, a visualization component that is suitable and manageable for an undergraduate student.

Research area: Energy data engineering, EV charging load profiling, demand forecasting, grid interaction modeling, and time-series analytics for smart grid applications.

Student roles:
Weeks 1–3: Data Architecture & Public Dataset Analysis
• Survey and acquire public EV charging datasets
• Design the data schema and ingestion pipeline for testbed integration
• Perform exploratory data analysis: charging duration, power demand, session frequency distributions
Weeks 4–6: Model Development (Simulated Data)
• Build time-series demand forecasting models
• Develop load profiling and grid impact assessment tools
• Prototype a demand-responsive smart charging scheduler
Weeks 7–10: Live Testbed Integration
• Deploy data pipeline on the Secure EV testbed
• Collect and curate real charging session data
• Validate and retrain forecasting models on live data
Weeks 11–12: Analysis & Dissemination
• Produce comparative analysis: simulated vs. live data model performance
• Prepare open dataset release and technical report
• Contribute to joint research publication

Skills required:
Academic Background: Computer Science, Data Science, Electrical Engineering, or related field. Python, data pipeline design, time-series analysis, SQL or NoSQL databases

Research area: My research investigates how indoor light environments — particularly daylight access, spectral quality, and temporal exposure patterns — influence occupant health and wellbeing. My research group develop data-driven, evidence-based approaches that connect building design parameters to physiological and psychological outcomes including sleep quality, mood, alertness, and visual comfort, using a combination of environmental sensing, wearable technologies, machine learning, and participatory design methods.

Student roles:
The students will contribute to a pilot research phase profiling real-world indoor lighting environments across home and workplace settings in Calgary, working closely with the principal investigator and research team. The students will assist in deploying and operating spectral light sensors and actigraphy wearables in diverse field settings, collecting high-resolution data across representative times of day and lighting conditions. They will annotate collected datasets with building parameters including window-to-wall ratio, window orientation, and electric lighting type, and contribute to building and validating a machine-learning classifier that maps spectral signatures to indoor lighting typologies.
The students will also assist in piloting mobile micro-surveys assessing mood, alertness, sleep quality, and visual comfort, and contribute to refining survey instruments to balance scientific rigor with participant burden. All human subjects research will be conducted under an approved University of Calgary Research Ethics Board protocol, in place prior to the start of the program — the students will not need to navigate this independently.
Additionally, the students will contribute to data analysis, participate in regular team meetings, and present their work to the team and external collaborators. There is potential for contribution to research reports and manuscript preparation.
Throughout the program, the student will gain hands-on experience in environmental sensing, wearable technology, machine learning, and field research methods at the intersection of building science, data science, and occupant health.

Skills required:
The ideal candidate is a senior undergraduate or graduate student in building science, electrical or computer engineering, data science, or a related discipline. Experience with machine learning, data analysis, or Python programming is required for the classifier development component. Familiarity with environmental sensing, light measurement, or wearable technologies is an asset. An interest in human health, indoor environments, and the intersection of technology and building design is essential. Strong organizational and communication skills are important given the field data collection component of the project.

Research area: My research focuses on intelligent communication and networking systems for B5G/6G networks. Areas of interest include intelligent IoT systems, semantic communications, multi-access edge computing (MEC), edge intelligence, digital twin-assisted networking, joint communication-computing resource management, and AI-native network automation. Our team investigates the integration of machine learning, generative AI, large language models, and agentic AI into wireless networks to enable autonomous, efficient, and resilient network operation. The goal is to develop scalable and intelligent network architectures that support future connected, data-driven, and service-oriented applications.

Student roles:
The student will play an active role in the design, development, and evaluation of data-efficient GAI-assisted semantic communication systems for future 6G networks. The project will provide hands-on research experience at the intersection of wireless communications, semantic communications, generative AI, and machine learning.

The student will begin by conducting a comprehensive literature review on semantic communication, generative AI, large language models, diffusion models, and AI-native wireless networking. Based on this review, the student will contribute to the design of novel GAI-assisted semantic transceiver architectures that improve semantic recovery while reducing communication overhead.

The student will assist in developing and implementing semantic encoding, knowledge representation, and generative semantic reconstruction algorithms. Particular emphasis will be placed on investigating how generative AI models can compensate for incomplete or mismatched knowledge at the receiver and improve semantic accuracy under bandwidth-limited and unreliable wireless channels. The student will also explore model acceleration, compression, and distillation techniques to enable efficient deployment on resource-constrained edge servers and IoT devices.

Additional responsibilities include developing simulation platforms, conducting experiments and performance evaluations, analyzing communication and semantic performance metrics, comparing the proposed approaches with existing methods, and identifying opportunities for further improvement. The student will participate in regular research meetings, collaborate closely with the research team, and contribute to the preparation of technical reports, research publications, and project presentations.

Through this project, the student will gain valuable experience in semantic communications, generative AI, machine learning, wireless networking, scientific research methodologies, data analysis, and technical communication skills, preparing them for future research and industry careers in next-generation intelligent communication systems.

Skills required:
We welcome highly motivated students with strong interests in the above research areas to join our team and contribute to high-impact research. Applicants should have a background in Communication Engineering, Computer Science, or related fields, with an undergraduate GPA of at least 3.3. Strong analytical skills, solid mathematical foundations, programming proficiency (e.g., MATLAB, Python), and effective communication and teamwork skills are essential.

Research area: My research focuses on developing machine learning methods for big data analysis and creating user-friendly web platforms and mobile applications to support data collection, processing, and interpretation.

Student roles:
The student will play an active and integral role in every stage of the design and development process for both the web platform and mobile application. This includes participating in initial planning and system architecture design, where the student will help define project requirements, user interface features, and data workflows. They will be expected to contribute creative ideas to improve usability, performance, and scalability of the application, ensuring that the platform is intuitive and accessible to a broad range of users.

On the technical side, the student will be responsible for both front-end and back-end development tasks. This involves creating user interfaces using modern web technologies such as HTML, CSS, JavaScript, React, and TypeScript, as well as building server-side logic and data management systems using Python or similar languages. The student will also develop mobile app components for Android and iOS using cross-platform frameworks (e.g., React Native or Flutter), ensuring consistency and responsiveness across devices.

Throughout the project, the student will implement, test, and refine features related to data input, visualization, and basic analysis tools. They will also help integrate secure data storage solutions and contribute to building APIs for communication between the web and mobile platforms.

Collaboration and communication will be key aspects of the student’s role. They will work closely with the project supervisor and possibly with other team members to ensure progress aligns with research goals. Regular code reviews, documentation, and iterative feedback will be part of the development process.

This hands-on role offers the student a unique opportunity to gain real-world experience in full-stack development, mobile programming, and applied data science, while contributing meaningfully to a research platform designed for large-scale data collection and analysis.

Skills required:
The ideal student should have experience in developing web platforms or mobile applications for both Android and iOS. Proficiency in front-end and back-end development is essential, with familiarity in technologies such as HTML/CSS, JavaScript, React, TypeScript, and Python. Experience with mobile frameworks (e.g., React Native or Flutter) and an understanding of responsive design, APIs, and database integration will be considered strong assets. The student should be comfortable working independently on full-stack development tasks and be eager to apply their technical skills to build practical tools for big data collection and analysis.

Research area: My research develops secure, accessible, and sustainable AIoT and UAV-based cyber-physical systems for industrial automation and emergency services. I integrate AIoT sensing, autonomous UAV platforms, computer security, and quantum-inspired optimization to advance technologies that enhance environmental monitoring, operational safety, and digital accessibility. Current research directions include: - AIoT-UAV enabled smart farming and emergency response - AIoT and quantum-inspired human-technology interaction for accessibility - Preserving privacy from AI assistants - Decentralized Web3 solutions for resilient and transparent networks. My work emphasizes practical, real-world impact while training students in AIoT, UAV systems, cybersecurity, and emerging decentralized technologies.

Student roles:
The student will contribute to the design, implementation, and evaluation of a decentralized Web3 peer-to-peer (P2P) social networking system. Their work will focus on building core components that support resilience, transparency, and data sovereignty.

The student will help design system modules involving decentralized identifiers (DIDs), P2P communication protocols, and distributed storage (e.g., IPFS/Filecoin). They will implement features such as node discovery, message routing, content replication, and DID-based authentication. The role includes writing and testing code in languages such as JavaScript/TypeScript, Python, or Go, and using Web3 libraries to interact with smart contracts for governance and integrity verification.

The student will run resilience experiments, simulating node churn, censorship attempts, and network partitions to measure system stability and data availability. They will analyze transparency through on-chain logs and evaluate data sovereignty metrics such as user control and content portability.

The student will also assist with documentation, contribute to a research paper, and support open-source release of the prototype.

Skills required:
The student should have a background in computer science, software engineering, or electrical/computer engineering, with general familiarity in distributed systems, networking, or Web3 concepts. Basic experience with programming (e.g., Python, JavaScript/TypeScript, or Go) is required. Exposure to blockchain, peer-to-peer networking, or decentralized storage is helpful but not mandatory. The student should be comfortable learning new technologies, contributing to prototype development, and assisting with system testing and evaluation. Strong analytical skills and an interest in decentralized or privacy‑preserving systems are desirable.

Research area: My research develops algorithms for decision-making under uncertainty, with applications to networked systems, such as smart grids, cloud computing and LLM inference systems. The central theme of my work is to understand how algorithm performance scales with the structural properties of the underlying systems (e.g., objectives, constraints, online dynamics, and multi-agent interactions), and the nature of environmental uncertainties (e.g., adversarial, stochastic, or predictive uncertainty). My goal is to design algorithms with provable guarantees for efficiency, robustness and fairness, enabling reliable resource allocation and scheduling in real-world networked systems.

Student roles:
The student will play an active role in the design and implementation components of the project. The main responsibility of the student is to design, implement and evaluate decision-focused learning methods for optimization of distributed energy resources under uncertainty. The student will first study the basic formulations of the target optimization problems, starting from storage arbitrage, where an operator needs to predict uncertain electricity prices and make charge/discharge decisions accordingly. Based on this formulation, the student will implement conventional two-stage methods that treat prediction and optimization separately, and compare them with decision-focused learning methods that directly account for the downstream optimization loss during training.

A major part of the project will focus on building the end-to-end decision-making pipeline. This includes collecting or constructing suitable datasets, preprocessing electricity price and energy demand data, implementing prediction models, integrating them with optimization solvers, and building simulation environments for evaluating the resulting decisions. The student will also implement methods that incorporate uncertainty quantification, such as calibrated uncertainty estimates, into downstream robust optimization models to improve risk control. Based on the initial storage arbitrage setting, the student may further extend the implementation framework to more general DER management problems involving uncertain renewable generation, electric vehicles, and uncertain consumer demand.

The student will also participate in regular research meetings, present progress, discuss technical challenges, and help prepare research outputs, including project reports, presentation materials, and potential conference or journal submissions.

Skills required:
The student is expected to have a solid mathematical background, including linear algebra, calculus, and probability. The student should also have basic knowledge of machine learning and optimization. Prior experience in power systems or smart grids is preferred but not required.

The student should have strong programming skills and be proficient in at least one programming language, such as Python. Experience with simulation tools or machine learning frameworks is preferred.

Research area: federated learning, neural networks,

Student roles:
Implementing algorithms, experimenting data, writing research reports.

Skills required:
Programming languages, statistics, set theory

Research area: My research fields are mostly about artificial intelligence (AI), machine learning, deep learning, natural language processing, and their applications. My recent research interest is in developing advanced deep learning models for understanding the semantics in structured data such as sequential signals, natural language text, and image data, and providing more intelligent interactions and analysis to target information systems. As part of my long-term research goal, this research project studies the state-of-the-art deep learning algorithms and agentic AI for jointly understanding and generating natural language and images for supporting semantics-aware interactions and reasoning in healthcare and educational information systems.

Student roles:
* retrieve relevant medical text and image data from open domain and open research resources.
* apply open source natural language processing tools including large language model based tools to text processing and model training and testing.
* use open source image processing tools for image processing and recognition.
* design selected deep generative models and algorithms for analyzing healthcare text and image data
* perform experiments for evaluating the designed algorithms and tools.
* develop a prototype system for demonstrating the integrated tasks of the application.
* write progress and development reports, and research papers.

Skills required:
* Major in Computer Science.
* Excellent skills in developing Python with advanced data structures and algorithms.
* Knowledge in artificial intelligence such as machine learning, deep learning, large language models, natural language processing and/or computer vision.
* Strong interest in learning exploring new models and algorithms of machine learning.
* Familiar with Linux programming and development environment.

Research area: My specialized research interests lie in Software Engineering and Software Quality. Through my research, I aim to facilitate software development, maintenance, and evolution using machine learning and develop new methods and techniques. More specifically, I focus on analyzing software artifacts (e.g., design, source code, documentation, requirements, etc.) using machine learning and natural language processing techniques, assessing the quality of software design and implementation, analyzing and prioritizing software requirements, and managing technical debts or issues.

Student roles:
* Assist in the design, implementation, and evaluation of deep learning models for malware detection and classification using multimodal datasets.
* Conduct data collection, preprocessing, feature engineering, and experimental analysis to improve the accuracy and robustness of cybersecurity detection systems.
* Review relevant research literature, document experimental findings, and contribute to technical reports, publications, and presentations related to the project.

Skills required:
* Strong programming skills in Python, including experience with machine learning and deep learning frameworks such as PyTorch or TensorFlow.
* Knowledge of cybersecurity fundamentals, particularly malware analysis, malware classification, and common attack techniques such as polymorphic and zero-day malware.
* Experience with data preprocessing, feature extraction, and multimodal data analysis involving static, binary, and image-based malware representations.
* Familiarity with deep learning concepts such as convolutional neural networks (CNNs), transfer learning, model evaluation, and performance optimization.
* Strong analytical and research skills, including the ability to read scientific literature, conduct experiments, interpret results, and communicate findings effectively in technical

Research area: We explore a novel type of survival prediction model that computes Individual Survival Distributions (ISDs) for endpoint likelihood prediction in several important tasks. ISDs provide patient-specific survival probabilities for each time point into the future from patient attributes. Clinicians could use these personalized survival estimates to prioritize hospital admissions, avoid under-or over-treatment by assessing patient-specific covariates and provide actionable insights to patients to alleviate some of their risk factors through lifestyle modifications. Our work is core to precision medicine; we anticipate that these algorithms could be used for patient triage and personalized healthcare management in the future.

Student roles:
The student will be primarily responsible for developing multimodal deep survival models for forecasting clinically significant events, such as rehospitalization, major adverse cardiovascular events, and mortality, using ECG / ECHO, or EHR data. The project will leverage modern machine learning frameworks such as PyTorch or TensorFlow.

The student will be involved in the some of following tasks:
Data Integration: Align longitudinal EHR data (diagnoses, procedures, medications, laboratory measurements), together with ECG / ECHO, and demographic information.
Method Development: Investigate deep survival and time-to-event modeling approaches, including N-MTLR, DeepHit, transformer-based methods, and multi-event forecasting frameworks.
Model Training: Develop individualized survival distribution (ISD) models for patient-specific risk prediction and longitudinal trajectory modeling.
Model Evaluation: Assess performance using survival-specific metrics, calibration methods, and clinically meaningful evaluation frameworks.
Analysis and Interpretation: Examine how different modalities and clinical variables influence survival curves and predicted event timing.
Literature Review: Conduct a review of deep survival modeling and multimodal learning approaches and compare proposed methods with benchmark models.

Timeline
Month 1: Conduct literature review, formulate research questions, and develop multimodal data pipelines.
Month 2: Prototype and train survival models, evaluate performance, and present interim findings.
Month 3: Refine models and analyses, prepare the final report, and deliver the final presentation.

The student is expected to work independently and collaborate with mentors in artificial intelligence and clinical informatics to iteratively develop and validate the proposed models.

Skills required:
The ideal student should have a strong background in statistical machine learning and an interest in healthcare applications. Familiarity with survival analysis methods (e.g., Cox models, Kaplan–Meier curves) and individualized survival distributions (ISDs) as well as mathematical knowledge of model evaluation methods, including concordance indices and calibration metrics are important. Proficiency in Python and deep learning frameworks, along with experience in time-series modeling and prior exposure to EHR data and physiological signals such as ECG or ECHO is an asset. Strong problem-solving and communication skills and the ability to work independently and collaboratively, are important.

Research area: My group (https://phulab.org/) develops and applies novel artificial intelligence (mainly deep learning) and large-scale statistical algorithms for various topics related to health data science. We also collaborate very closely with local, national and international life science scientists and clinicians on different omics projects. We are actively working in the following four research areas. (1) Deep Learning and Visual Analytics Algorithms for Omics Data, Imaging, and Electronic Medical Records; (2) Deep Learning for Drug Discovery (3) Integrative Analysis Frameworks for Omics Data, Imaging, and Electronic Medical Records (4) Translational Medicine and Precision Health

Student roles:
• Implement computational and statistical algorithms proposed by the principal investigator in the project
• Test the algorithms using the collected data sets and improve the algorithms
• Document the algorithm details and analysis results
• Make a public presentation on the internship work
• Write a manuscript or report for a potential journal or conference submission

Skills required:
• Senior undergraduate students in Bioinformatics, Computational Biology, Computer Science, Statistics, Biostatistics, mathematics, engineering or a related quantitative field
• Basic programming skills using R (required), Python (required), Perl (optional), MATLAB (optional).
• Experience in handling big data sets
• Understand the basic principles of machine learning and deep learning

Research area: My areas of research are computer vision and machine learning for processing complex and unstructured data. In particular, the use of new approaches such as Deep Learning and multimodal imaging in the fields of security and biometrics, UAVs, medical imaging and intelligent robotics.

Student roles:
Reading of some documents related to the subject.
Developing vision and machine learning algorithms using existing libraries.
Testing with available datsets.
Writing documentation/report on the work done.

Skills required:
Programming (Python, Matlab, etc.)
Basic knowledge of Matrix computations
Machine leaning or image processing are considered a plus

Research area: computer vision and applications

Student roles:
You are expected to work with a graduate student/Postdoc researcher, get familiar with state-of-the-art deep learning techniques, and gain hands-on research experience on benchmark and home-grown datasets. There are ample opportunities to be involved in exciting research topics in computer vision and to publish research findings at top-tier conferences and journals.

Skills required:
Necessary image processing background and working knowledge of calculus and linear algebra; Good programming skill in python/c++.

Research area: Data science, Machine learning, Time series analysis, Pattern recognition.

Student roles:
System implementation, data analysis

Skills required:
Reinforcement learning, time series analyses, pattern recognition, programming, and some basic financial knowledge.

Research area: We are Computer Vision for Smart Structure (CViSS) Lab, an interdisciplinary research group at the University of Waterloo. Our overarching goal is to develop smart infrastructure by exploiting and embedding technology in sensing, computing, and artificial intelligence (AI) to gather and analyze unobservable information regarding the condition, safety, and resiliency of our infrastructure.

Student roles:
The intern will actively contribute to the research project by performing any or a combination of the following (but not limited to) key tasks under the supervision of the research team:
- Develop and Implement Deep Learning Models: Assist in coding, training, and evaluating computer vision models (using Python/PyTorch/TensorFlow) for core tasks including building detection, semantic segmentation, and monocular depth estimation from street-view and aerial imagery datasets.
- Preprocess and Augment Geospatial Data: Prepare large-scale image datasets for model training, including georeferencing, normalization, and developing data augmentation pipelines to enhance model robustness for hazard scenarios.
- Assist in Multi-View Fusion Experiments: Implement and test prototype architectures (developed by the team) designed to fuse information from terrestrial street-view and overhead aerial imagery for improved 3D understanding or damage assessment.
- Conduct Model Evaluation and Analysis: Rigorously test model performance using quantitative metrics (e.g., IoU, mAP, RMSE) and qualitative analysis on held-out validation sets, documenting results and identifying failure modes.
- Research Support: Conduct literature reviews on specific computer vision techniques relevant to the project (e.g., depth estimation methods, generative models for synthesis, multi-modal fusion approaches) and summarize findings.
- Documentation and Reporting: Maintain clear and organized code repositories, document experimental procedures and results, and contribute to the preparation of progress reports and research presentations.
- Publication: Potentially co-author top-tier journal and/or conference venues in robotics or computer vision such as CVPR, ICCV, ICRA, IROS, etc. depending on the contribution.

Skills required:
Anyone with an ongoing undergraduate degree in a related (or unrelated with strong project experience) is welcome to apply.
Demonstrated experience (via publications, projects, or code) in:
- Deep Learning for Computer Vision (CNNs, Transformers - ViT, DETR, Mask R-CNN, etc.).
- 3D Computer Vision (Monocular depth estimation, SfM, SLAM).
- Multi-modal/view Data Fusion techniques.
- Generative AI (GANs, VAEs, or NeRFs) for image synthesis or reconstruction.
- Expert-level coding in Python and deep learning frameworks (PyTorch - essentia).
- Data Handling: Experience working with large image datasets and geospatial data formats (e.g., GeoTIFF, point clouds).

Research area: My specialized research interests lie in Software Engineering and Software Quality. Through my research, I aim to facilitate software development, maintenance, and evolution using machine learning and develop new methods and techniques. More specifically, I focus on analyzing software artifacts (e.g., design, source code, documentation, requirements, etc.) using machine learning and natural language processing techniques, assessing the quality of software design and implementation, analyzing and prioritizing software requirements, and managing technical debts or issues.

Student roles:
- Perform a literature review on design patterns and antipatterns for API design
- Define the template for the patterns and antipatterns
- Following the template, define linguistic patterns and antipatterns for API design
- Write a final report with the linguistic patterns and antipatterns defined for API Design

Skills required:
- Strong English communication, writing, and presentation skills
- Creative in design, modeling, and visualization
- Familiarity with technical writing
- Some basic understanding of linguistic patterns and antipatterns for API design (do some initial background research)

Research area: My long-term research objective focuses on intra-operative target tracking for radiotherapy. In fact, real-time assessment and feedback enable clinicians to make necessary adjustments and improve the outcome of the procedure. I have a decade of experience in biomedical computing and medical devices product research, development and management. Before becoming an assistant professor at Bishop’s University and then at Toronto Metropolitan University, I used to work for a leading company in the radiotherapy industry. My research goals focused on organ tracking using medical images.

Student roles:
Develop and implement methods.
Apply the methods on datasets.
Analyze and document results.
Write conference/ journal paper.

Skills required:
Knowledge of Python.
Strong logic and fundamentals in statistics

Research area: My research focuses on AI-assisted software engineering and applied agentic AI, alongside my core work in embedded and cyber-physical systems. I study how large language models, retrieval-augmented generation, and multi-tool AI agents can operate in real-time, latency-sensitive settings such as voice telephony, where the system must perceive, plan, and act within sub-second budgets. Residential property management offers a rich test bed for this research direction. It combines spoken natural-language input from tenants, grounded reasoning over lease documents and policies, multi-step downstream actions, and a clear need for human oversight of consequential decisions, all under strict real-time conversational constraints.

Student roles:
The intern will own the design and implementation of the agentic AI system from end to end, including the scoping of system requirements, the design of the knowledge base, and the choice of evaluation benchmarks. The supervisor will provide research direction, weekly mentorship, and writing guidance throughout the project.

Weeks 1 and 2: Onboarding, literature review on voice-first conversational agents, retrieval-augmented dialogue, tool-use evaluation, and human-AI oversight. The intern will study current property-management workflows from public sources, define the scope of the system, and produce a refined system architecture that includes the latency budget for each pipeline stage.

Weeks 3 and 4: Design and construct a fabricated property data set, including units, leases, tenants, vendor lists, and payment ledgers populated with realistic but entirely fictional information. Set up the telephony integration using Twilio Voice or equivalent, including the test phone number and call-routing logic.

Weeks 5 and 6: Implement the streaming speech-to-text and text-to-speech pipeline using Whisper or Deepgram. Tune for end-to-end voice-turn latency. Implement barge-in handling so the tenant can interrupt the agent naturally during conversation.

Week 7: Integrate the large language model agent layer using LangChain, LangGraph, or a direct tool-use API. Implement each tool the agent invokes, including retrieval over leases and policies, maintenance-request triage, scheduling lookup, and the post-call action package generator.

Week 8: Build the human-approval dashboard. The supervisor and the intern will use it to review and approve action packages produced after each call.

Weeks 9 and 10: Design and run a benchmark of scripted call scenarios. Evaluate the system on task-completion rate, latency distribution, intervention frequency, and failure modes.

Weeks 11 and 12: Conduct statistical analysis. Draft a short paper. Prepare the open-source release with documentation.

Skills required:
Strong Python programming and prior experience with at least one large language model API such as Anthropic, OpenAI, or open-source models served through Ollama. Familiarity with retrieval-augmented generation pipelines and agentic frameworks such as LangChain or LangGraph is important. Experience with real-time or streaming systems is highly preferred, since voice telephony is latency-sensitive. Comfort with at least one telephony or speech service such as Twilio, Vonage, Deepgram, or Whisper is an asset but can be learned in the first week. Comfort with Git, Linux, and basic web back-end development is required. Interest in AI safety guardrails is strongly valued.

Research area: My specialized research area focuses on emotionally intelligent, context-aware, and multimodal AI systems for assistive technologies and human-centred computing. My work integrates affective computing, multimodal machine learning, human-computer interaction, and adaptive intelligent systems to support personalized engagement and cognitive interaction in healthcare and assistive environments. Specifically, my research investigates the use of interpretable AI architectures such as Behaviour Trees, multimodal interaction modelling, and safety-aware decision frameworks for socially assistive robots. This project focuses on developing a Behaviour Tree–based safety architecture for the Temi V3 robot to support predictable, modular, and explainable interaction management within assistive robotics applications.

Student roles:
The student will play an active role in the design, implementation, and evaluation of a Behaviour Tree–based safety framework for socially assistive robot interaction using the Temi V3 platform. The student will contribute to both the research and technical development aspects of the project while gaining hands-on experience in robotics, artificial intelligence, software engineering, and human-computer interaction.
The student will begin by conducting a literature review on Behaviour Trees, socially assistive robotics, interaction safety, and explainable AI systems. Following this, the student will assist in designing modular Behaviour Tree architectures that manage interaction states, handle interruptions, support fallback safety behaviours, and enable controlled behavioural transitions in human–robot interaction scenarios.
A major component of the role will involve software development and prototype implementation. The student will work with the Temi SDK and related development tools to integrate Behaviour Tree logic into the robot interaction workflow. Tasks may include implementing interaction-state management modules, configuring voice-triggered interactions, developing simple monitoring and logging mechanisms, and testing behavioural responses under predefined scenarios.
The student will also participate in controlled testing and evaluation activities to assess behavioural consistency, system responsiveness, and safety-aware interaction performance. The role will involve documenting system architecture, maintaining development records, and contributing to technical reports and presentations related to the project.
Throughout the internship, the student will work closely with the research supervisor and project team through regular meetings, milestone reviews, and collaborative problem-solving sessions. The project will provide the student with practical exposure to applied AI and robotics research while developing technical, analytical, and research communication skills within an interdisciplinary assistive technology environment.

Skills required:
The ideal student should have a background in Software Engineering/Computer Science/Robotics/Artificial Intelligence, or a related field. The student should possess foundational programming skills in Python or Java and an interest in robotics, human-computer interaction, or intelligent systems. Familiarity with software development tools, API integration, and basic AI concepts would be beneficial. Experience with Android Studio, behaviour modelling, or robot SDKs is considered an asset but is not mandatory. Strong problem-solving, communication, and teamwork skills are important, as the student will participate in collaborative research and prototype development activities involving socially assistive robotics and safety-aware interaction systems.

Research area: John Kildea is an associate professor of Medical Physics in the Gerald Bronfman Department of Oncology at McGill University. John’s research focuses on building software for patient-centered health informatics and experimental methods to examine the biophysics underlying radiation-induced carcinogenesis. At the Research Institute of the McGill University Health Centre, John directs the research, development, and technology innovation activities for the Opal Health Informatics Group (O-HIG). John is the principal investigator of the Quebec SmartCare Consortium project (quebecsmartcare.com). John's research group created the open-source patient-in-the-loop data platform Opal (opalmedapps.com).

Student roles:
In this project, we will design and implement a novel platform to provide personal health information (PHI) data pods using the Solid framework. We will provide a mechanism for patients to create their own pods and securely store their PHI in them from the Opal patient portal (opalmedapps.com). We will work with patients and healthcare data security and governance experts to ensure that all relevant protocols are put in place for the storage and access to health data. We will imagine a new world where patients have control over their healthcare data and we will examine the feasibility of developing our solution at scale.

The student will work with the researcher and his research group to design and develop a data-control platform for PHI using Solid. S/he will work with the Opal Health Informatics Group (the software development team behind the Opal patient portal) to understand the requirements for data access and control and all the cybersecurity protocols that need to be put in place to implement the solution within the Opal patient portal.

Skills required:
Experience with data handling and software development, ideally using Python and Django.
Experience with Javascript, SQL would be useful.
Some knowledge of cybersecurity would be a benefit.
An understanding of the Solid framework would be a benefit.
An understanding of healthcare data would be a benefit.

Research area: Dr. Hossein Rouhani is an Professor in the Department of Mechanical Engineering, at the University of Alberta. His research areas are human motion measurement, musculoskeletal biomechanics, biomedical instrumentation design, biomedical signal processing, wearable technologies, orthopaedic and rehabilitation engineering, gait and posture assessment, neural control systems. The application of Dr. Rouhani's research and technology development is in medical diagnosis devices, rehabilitative technologies, athletic performance assessment, work-related-injury prevention, and fall detection. Dr. Rouhani collaborates with a number of university hospitals across Canada within his interdisciplinary and translational research program.

Student roles:
The student will:
1) learn about principals of the wearable robots and human motion biomechanics, and biomechatronics.
2) desgin, simulate and potentially fabricate the hardware of wearable robots.
3) measure biomechanical parameters upper and lower limbs motion trajectory during daily life and develop computer controllers for the robots.
4) test the technology in motion measurement laboratory, conduct the experiments on human subjects and will process the collected motion data.
5) develop software packages for the user of the developed technology.
6) potentially have the opportunity to interact with users (patients) at the university’s affiliated hospitals and conduct experiments with thier participation.
7) prepare the detailed report on the project at the end of the internship.

Skills required:
The student must be familiar with computer programming using MATLAB and engineering mechanics. Knowledge on mechatronics is an asset. Electrical, Biomedical, Mechanical, Computer Engineering students would typically have the required background. The student must be familiar or willing to learn data acquisition, physiological measurements, and data/signal processing, human motion biomechanics.

Research area: Generative AI, healthcare, software development

Student roles:
1) Analyze the literature relevant to the proposed project
2) Help design the LLM-based prototype
3) Help implement and deploy the LLM-based prototype
4) Help carry out experiments on the LLM-based prototype to assess its performance
5) Write a research paper to report the outcomes of the project.

Skills required:
1) Be fluent in English
2) Have excellent writing and oral skills
3) Have a basic knowledge of Generative AI technologies (e.g., Gemini, Llama, GPT)
4) Be very creative
5) Be team-oriented
6) Have excellent analytical skills
7) Master the Python programming language
8) Be enrolled in a computer science program.

Research area: This research lies at the intersection of Human–Computer Interaction (HCI), augmented reality (AR), and digital mental health. It focuses on designing interactive AR systems that support mindfulness and well-being within everyday activities, rather than through dedicated, formal activities like meditation. Informal mindfulness has demonstrated benefits but is difficult to sustain, creating opportunities for technology to scaffold attention and engagement. AR systems enable subtle, context-aware guidance embedded within real-world tasks and this research investigates how such systems can guide users toward mindful awareness while preserving autonomy, intrinsic motivation, and the experiential qualities essential to meaningful mindfulness practice.

Student roles:
The student will contribute to the design, development, and early-stage evaluation of an augmented reality (AR) system that supports informal mindfulness in everyday activities.

On the development side, the student will assist in extending an existing AR prototype or implementing a lightweight system that explores different styles of mindfulness guidance (see above). Depending on project scope, this may include implementing interaction features, integrating visual or auditory cues, and supporting basic data logging. The student will not be expected to build a complete system independently, but rather to contribute to specific features or components aligned with the project goals.

On the research side, the student will assist with preparing and conducting a small-scale user study. This may include helping develop study materials, supporting participant sessions, and collecting data. The student will also contribute to data analysis, including summarizing questionnaire responses, examining behavioral patterns, and assisting with qualitative analysis of interview data.

Tasks will be scoped to match the 12-week internship timeline, with flexibility to focus on either development, evaluation, or both. The goal is to ensure that the student produces a meaningful and well-defined contribution within a manageable project scope.

Throughout the internship, the student will gain experience in human-centered design, AR development, and experimental research methods. They will work closely with the supervising research team, participate in regular meetings, and contribute to project documentation. Where appropriate, the student may also contribute to a research report or publication.

Skills required:
Applicants should have a background in Computer Science, Human–Computer Interaction, or a related field. Experience with programming (e.g., C#, Python, or JavaScript) is required, along with familiarity with user-centered design or experimental evaluation methods. Experience with AR development (e.g., Unity, ARKit, or ARCore) is an asset but not required. Strong communication skills and an interest in health, wellness, or digital mental health are desirable. Students should be comfortable working both independently and collaboratively in a research environment.

Research area: My research area is in Human-Computer Interaction (HCI), with a focus on developing and studying novel interactions with mobile and wearable devices. More specifically, I am interested in exploring users’ needs and limitations while they are interacting with emerging technologies (e.g., smartphones and smart glasses). I tackle these limitations by designing new interfaces and interaction techniques. My methodology is rooted in HCI techniques, where I adopt quantitative and qualitative evaluations to validate the interaction techniques.

Student roles:
The student will be mainly involved in the design and development cycles. S/he will be a part of running user studies to evaluate the efficiency of the developed solution. Along with the help of undergraduate, graduate students, and other collaborators, the student will analyze data and write reports to publish the results in prestigious conferences/journals. These applied experiences in design and analysis methods will be extremely beneficial for the student as these will help them in preparing for their future careers as researchers, designers, or software developers for many top Canadian and worldwide academy and industry.

In addition to concrete research experiences, the student is expected to attend on-campus seminars and workshops to gain knowledge on contemporary subject matters. The student will be expected to participate in regular lab meetings where the student will be given a chance to present his/her ideas, discuss research progress, and receive constructive feedback. Additionally, the intern will be encouraged to build industry and academy connections in Canada. Such a collaborative and supportive environment would help the student to become successful in their future career.

Skills required:
Undergraduate students with computer science or engineering backgrounds are welcome to apply. The proposed project requires the student to develop a Virtual Reality (VR) application to track users' hand movements. Thus, prior experience with VR application development is preferred. A good understanding of hand-tracking solutions (e.g., MRTK Hand tracking) would be instrumental for the position. The intern should have the skills to solve complex problems and troubleshoot issues for potential solutions. While the intern will be expected to work in a team environment, s/he needs to have strong communication skills, both when speaking to people and in writing.

Research area: Our group focuses on information visualization and personal health informatics, studying how everyday users interpret continuous physiological data from consumer wearables — heart rate, sleep, activity, and stress. We combine visualization design principles, user-centered design methods, and empirical evaluation to develop mobile-first representations for small touch screens. We are particularly interested in glanceability: designing charts that communicate key patterns and anomalies in under two seconds, without sacrificing the ability to explore detail when needed.

Student roles:
In weeks 1–2, the intern will review literature on mobile visualization, glanceability, and personal health informatics, and conduct a formative analysis of a sample wearable dataset to identify design opportunities. In weeks 3–5, the intern will produce low-fidelity sketches and high-fidelity prototypes of candidate designs, refining them through supervisor feedback and informal think-aloud walkthroughs. In weeks 6–8, the intern will implement the finalized interactive designs as mobile prototypes and prepare the user study stimuli and task battery. In weeks 9–10, the intern will obtain ethics approval, recruit participants, and conduct study sessions. In weeks 11–12, the intern will analyze quantitative and qualitative data, prepare result visualizations, and write a final report. The intern meets weekly with the supervisor and will contribute directly to a conference paper manuscript.

Skills required:
Background in computer science, information visualization, or HCI. Proficiency in JavaScript with experience in D3.js, Vega-Lite, or a comparable visualization library. Familiarity with React Native or Flutter for mobile prototyping is a strong asset. Interest in personal health informatics and visual design. Strong written communication skills.

Research area: Database systems, vector search, storage management, RDMA

Student roles:
Responsibilities include but are not limited to:
1. Contributing to research in database engines by implementing and evaluating system prototypes;
2. Performing systems-level development in C/C++/Rust, producing high-quality, well-tested, and maintainable code;
3. Designing and running rigorous experiments, analyzing performance data, and identifying and resolving system bottlenecks;
4. Studying top-tier publications;
5. Presenting progress in group meetings.

The student is expected to work independently, meet milestones, and maintain a high standard of technical rigor. Strong performers may contribute to publications at premier data management or systems venues.

Skills required:
Applicants should:
1. Be proficient in C/C++/Rust;
2. Demonstrate strong academic performance in Operating Systems, Data Structures, and Database Systems (as reflected on their transcript);
3. Have prior project or internship experience in computer systems.

Research area: My research area is in Human-Computer Interaction (HCI), with a focus on developing and studying novel interactions with mobile and wearable devices. More specifically, I am interested in exploring users’ needs and limitations while they are interacting with emerging technologies (e.g., smartphones and smart glasses). I tackle these limitations by designing new interfaces and interaction techniques. My methodology is rooted in HCI techniques, where I adopt quantitative and qualitative evaluations to validate the interaction techniques.

Student roles:
The student will be mainly involved in the design and development cycles. S/he will be a part of running user studies to evaluate the efficiency of the developed solution. Along with the help of undergraduate, and graduate students, and other collaborators, the student will analyze data and write reports to publish the results in prestigious conferences/journals. These applied experiences in design and analysis methods will be extremely beneficial for the student as these will help them in preparing for their future careers as researchers, designers, or software developers for many top Canadian and worldwide academies and industries.

In addition to concrete research experiences, the student is expected to attend on-campus seminars and workshops to gain knowledge on contemporary subject matters. The student will be expected to participate in regular lab meetings where the student will be given a chance to present his/her ideas, discuss research progress, and receive constructive feedback. Additionally, the intern will be encouraged to build industry and academy connections in Canada. Such a collaborative and supportive environment would help the student to become successful in their future career.

Skills required:
Undergraduate students with backgrounds in computer science, engineering, or related fields are encouraged to apply. The project involves developing XR/VR applications; therefore, experience with VR/XR development, Unity, C#, or interactive application development is preferred. Experience with AI, HCI, or wearable technologies is also beneficial. The ideal candidate should have strong problem-solving and troubleshooting skills, be eager to learn new technologies, and communicate effectively in both team and research environments.

Research area: • Bioinformatics • Intelligent systems modeling and optimization • Software engineering for mobile platforms • Evolutionary computation • Image analysis and scientific visualization • Analysis, design, development and evaluation of training and education materials and programs in eLearning environments

Student roles:
• To further evaluate and enhance the SIMARD (A simulated annealing based RNA design algorithm) by incorporating new computational intelligence paradigms.
• To develop RNA structural comparison tools.
• To develop molecule vsualization tool

Skills required:
- Experience implementing algorithms in C, C++ or Java
- Basic knowledge in software engineering
- Interest in real-world applications of advanced algorithms
- Experience with computer graphics and scientific visualization will be a plus

Research area: My research area is in Human-Computer Interaction (HCI), with a focus on developing and studying novel interactions with mobile and wearable devices. More specifically, I am interested in exploring users’ needs and limitations while they are interacting with emerging technologies (e.g., smartphones and smart glasses). I tackle these limitations by designing new interfaces and interaction techniques. My methodology is rooted in HCI techniques, where I adopt quantitative and qualitative evaluations to validate the interaction techniques.

Student roles:
The student will be mainly involved in the design and development cycles. S/he will be a part of running user studies to evaluate the efficiency of the developed solution. Along with the help of undergraduate, graduate students, and other collaborators, the student will analyze data and write reports to publish the results in prestigious conferences/journals. These applied experiences in design and analysis methods will be extremely beneficial for the student as these will help them in preparing for their future careers as researchers, designers, or software developers for many top Canadian and worldwide academy and industry.

In addition to concrete research experiences, the student is expected to attend on-campus seminars and workshops to gain knowledge on contemporary subject matters. The student will be expected to participate in regular lab meetings where the student will be given a chance to present his/her ideas, discuss research progress, and receive constructive feedback. Additionally, the intern will be encouraged to build industry and academy connections in Canada. Such a collaborative and supportive environment would help the student to become successful in their future career.

Skills required:
Undergraduate students with computer science or engineering backgrounds are welcome to apply. The proposed project requires the student to design a system capable of delivering precise thermal feedback to the fingertips in real time. Thus, prior experience with thermal sensors or Arduino is preferred. The student will be provided with the necessary support (e.g., pairing with another student who is an expert in VR) to design the VR environment. The intern should have the skills to solve complex problems and troubleshoot issues for potential solutions.

Research area: Our research sits at the intersection of Human-Computer Interaction (HCI) and applied AI. We design, build, and evaluate interactive systems (particularly LLM-powered tools) that support people in two settings: everyday accessibility and inclusion (for example, intergenerational communication and digital access for older adults) and knowledge work and productivity (for example, AI-assisted sensemaking, ideation, and qualitative analysis). Our methods combine design-led prototyping with mixed-methods user research, including controlled studies, semi-structured interviews, and thematic analysis. We publish at venues such as CHI, UIST, and CSCW.

Student roles:
The student will work as a junior researcher across all stages of the human-computer interaction (HCI) research process, with responsibilities tailored to their strengths and interests. Tasks include:

1. Design and prototyping. Conduct targeted background research on a chosen problem area, synthesize findings into design opportunities, and produce wireframes, mockups, and interactive prototypes in tools such as Figma. The student is expected to take initiative in proposing design directions and iterating on supervisor feedback.

2. System development. Implement a working interactive prototype, typically a web-based application integrating AI components such as LLM APIs, MCP-based tooling, or custom inference pipelines. This includes front-end and light back-end development, version control via Git, and deployment to a cloud platform. The student will write clean, documented code suitable for open-source release.

3. User research and evaluation. Help plan and run a user study, including drafting study protocols, recruiting participants, preparing IRB-equivalent ethics materials, moderating sessions, and collecting data. Both quantitative measures (task performance, validated survey instruments) and qualitative data (interviews, think-aloud transcripts) are typically involved.

4. Analysis and dissemination. Analyze collected data with appropriate methods: descriptive and inferential statistics for quantitative data, thematic or open coding for qualitative data. Contribute to writing up results. Strong outcomes will be developed toward a co-authored submission to a top HCI venue such as CHI, UIST, CSCW, or DIS as a workshop paper, late-breaking work, or full paper.

5. Lab participation. Attend one-on-one meetings with the supervisor, present progress at lab meetings, participate in paper-reading discussions, and collaborate with graduate students and other lab members.

Skills required:
Strong applicants will have a background in computer science, HCI, design, or a related field, and a demonstrated interest in human-centered research.
Required: programming proficiency in JavaScript/TypeScript or Python, and basic web development.
Strongly preferred: experience with React or Node.js; familiarity with design tools such as Figma; comfort with Git/GitHub; and prior exposure to LLM APIs or AI integration.
Bonus: experience running user studies, surveys, or qualitative coding.
We especially value curiosity, written communication skills, and the ability to work both independently and collaboratively.

Research area: The Uncanny Valley theory describes the disturbing effect of imperfect human likenesses that have dominated HRI. Referring to the Uncanny Valley, social robots usually constitute a form of anthropomorphism or zoomorphism. For example, social robots typically behave like humans or animals, such as mimicry of human/animal behavior and emotional expression, with speech, gestures, movements, and eye-gaze features. Prior research found that it is much easier for an embodied humanoid robot with emotional expression to gain users’ trust to release personal information than a disembodied interactive kiosk. Therefore, this proposal focuses on designing and implementing zoomorphic robots.

Student roles:
The required role of the student would be tasked with pulling together materials for the literature review on these topics: social robot design and implementation.

Skills required:
The students should have a background in Business, Social Science or Computer Science/Information Technology.

Research area: Research is in the areas of human-computer interaction and domestic computing. Students should have a background in one of three areas: 1) Software design and development: write computer programs for mobile devices and/or embedded devices (e.g., using Raspberry Pi, Arduino); be able to use knowledge of people's needs to inform technology design 2) Industrial Design: designing, fabricating, and making physical forms and enclosures for the tangible embedded devices that we will create 3) User experience research: conducting studies of human behaviours and needs for technology; be able to design computer user interfaces via sketching.

Student roles:
The student will: use state-of-the-art embedded technologies and devices (e.g., raspberry pi, arduino) to prototype new user experiences; create connected devices that are robust and deployable in the field; apply industrial design and/or digital fabrication skills to give form to the tangible embedded forms of distributed domestic data objects that we create; test and evaluate prototypes through a design process as we move from early ideas to finalized finished forms. The student may also help plan and conduct research studies, e.g., interviews, field observations. Creating visual designs of a user experience and interface.

Skills required:
The student will perform research in the area of domestic computing to support rich, digitally-mediated experiences of reflection and reminiscence among family members living together. This involves the development and implementation of software on embedded devices, managing server-side and client-side network connectivity in and across connected devices, and/or designing, fabricating, and making physical forms and enclosures for the tangible embedded devices that we will create. It may also include conducting studies of human behaviours and needs for technology, and designing computer user interfaces via sketching and design software.

Research area: My research area is the multi-disciplinary field of human-computer interaction and human factors, which draws on knowledge from interface design, cognitive psychology, and experiment design. My goal is to better understand how people interact with displays in different conditions and then design better displays to suit their needs, especially in complex environments like driving. Eye tracking is a main tool that I use to understand the effects of different displays and conditions.

Student roles:
The student will have to read the literature on the topic, create the driving scenarios and AR-HUD for the experiment, run the VR study, and analyze the results. Running the study will involve obtaining ethics approval for the study with human participants, recruiting participants, collecting data in a lab environment, and then processing that data.

Skills required:
Students from a computer science background or a computer/electrical engineering background would be the best fit. Knowledge of Unity and familiarity with virtual reality and eye tracking would be a plus. A good knowledge of statistics and experiment design is also beneficial.

Research area: My research interests are in the disciplines of software engineering and applied artificial intelligence, with applications in cybersecurity. I investigate AI-driven approaches to detect software vulnerabilities, secure the growing class of systems built on large language models, including code-generating assistants and autonomous AI agents, and identify AI-generated and human-driven social engineering attacks. My work addresses the security of AI systems themselves, including vulnerabilities introduced by LLM code generation and the emerging risks associated with agentic AI, alongside the use of AI to detect and counter malicious content and manipulation online.

Student roles:
1. Curate a dataset of social engineering conversations from public sources.
2. Annotate the dataset by persuasion tactic (urgency, authority impersonation, trust-building).
3. Fine-tune an LLM-based classifier for multi-label tactic detection.
4. Build an interpretability layer that highlights the evidence behind each classification.
5. Evaluate the classifier's generalization across different platforms.

Skills required:
Strong Programming Competence – Python, with experience using Hugging Face Transformers or comparable NLP frameworks
Quantitative Skills – Probability and statistics sufficient for the evaluation of classification performance under class imbalance
Analytical Skills – Demonstrated knowledge of machine learning and natural language processing fine-tuning methodology
Other Relevant Skills – Experience with data annotation; familiarity with interpretability methods (e.g., attention visualization, SHAP); strong academic writing ability for technical reporting

Research area: My research spans data privacy, cloud computing, cybersecurity, social network analysis, and AI/machine learning. I have addressed challenges in safeguarding personal data in cloud environments and developed secure, reliable systems. I proposed a cloud solution incorporating an entropy-based trust computation approach for data-as-a-service environments. Additionally, I have worked on heterogeneous information networks, focusing on healthcare data privacy, where I developed a differentially private algorithm for releasing de-identified health data with reduced information loss and enhanced scalability. My work also includes contributions to user credibility systems in social networks, blockchain-based solutions, as well as supply chain cybersecurity risk analysis.

Student roles:
The student will play a central role in the design, development, and evaluation of a hybrid deepfake detection system tailored to the banking and financial services sector. The focus will be on detecting three key types of deepfake-enabled fraud: voice impersonation, synthetic identity creation, and executive fraud.

The student will begin by conducting a comprehensive literature review to understand current trends, threats, and detection approaches related to deepfakes in audio, image, and video domains. This review will inform the design of a taxonomy of deepfake attack vectors specific to the banking context.

Following this, the student will explore and benchmark existing machine learning and deep learning models for detecting manipulated audio (e.g., voice biometrics, spectrogram analysis), forged images and videos (e.g., deepfake face detection, liveness detection), and multimodal fusion techniques. The student will develop and fine-tune models using publicly available and synthetic datasets and, where applicable, use domain-specific datasets (e.g., voice recordings, KYC-style image data) to simulate banking-related fraud scenarios.

The student will also be responsible for developing a multimodal detection pipeline that combines features from both audio and visual inputs, aiming for improved detection accuracy and robustness. This includes preprocessing raw inputs, feature extraction, model training, and performance evaluation using metrics such as precision, recall, and F1-score.

Throughout the project, the student will be expected to document all stages of the research, contribute to writing a conference or journal paper, and present findings to academic and industry audiences where appropriate. The student should be comfortable working independently, collaborating with the research team, and integrating feedback constructively.

The role offers a unique opportunity to contribute to applied research with strong real-world impact in cybersecurity and financial fraud prevention.

Skills required:
- Strong background in machine learning and deep learning, especially CNNs and RNNs
- Experience with audio and image processing techniques
- Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch)
- Familiarity with biometric systems, voice recognition, or facial analysis
- Understanding of cybersecurity concepts, particularly in banking/finance
- Skilled in handling large datasets, including data preprocessing and augmentation
- Ability to conduct literature reviews, critically analyze academic papers, and integrate findings into the research process
- Ability to work independently and in a team
- Strong communication and academic writing skills for research papers and conference/journal submissions

Research area: My research focuses on artificial intelligence for health, particularly the development and application of AI methods for the prediction and management of complex health conditions. I work with longitudinal and multimodal health datasets, wearable sensor data, electronic medical records, and linked health data to support earlier prediction, prevention, and personalized care. My research addresses complex age-related, neurological, and acute care conditions, including dementia-related symptoms, Parkinson’s disease, stroke, and falls, as well as critical care outcomes. The broader goal is to develop clinically meaningful, interpretable, responsible, and equitable AI tools that can identify early risk markers and support proactive healthcare decision-making.

Student roles:
The Globalink intern will contribute to a focused 12-week project on the early prediction of a specific complex health condition or outcome. The exact use case will be determined before or at the start of the internship based on dataset readiness, feasibility, and the intern's background. Possible examples include dementia-related behavioural symptoms, Parkinson's-related symptoms, fall risk, or another age-related or neurological outcome aligned with the research program. All required approvals and data access arrangements will be in place before the internship begins.

Gaining hands-on experience across the machine learning pipeline, the student will:
• Complete a targeted review of the selected condition, outcome definition, and relevant machine learning approaches to inform the analysis.
• Work with the approved dataset (which may include structured longitudinal health data, medical imaging data, or a combination of data types) to perform data cleaning, variable selection, missing-data handling, image preprocessing, exploratory analysis, and visualization.
• Develop and train preliminary machine learning predictive models, evaluate model performance, and examine important predictors using explainability techniques.
• Maintain organized and reproducible code, participate in regular meetings, and contribute to the clinical and technical interpretation of the findings, summarizing the work in a final report and presentation.

Skills required:
The student should have a background in computer science, data science, biomedical engineering, applied mathematics, health informatics, or a related field. Strong programming skills in Python and machine learning are required. Prior experience with data cleaning, statistical analysis, deep learning, and visualization is highly desirable. Familiarity with health data, longitudinal datasets, medical imaging, missing-data handling, interpretable AI, or GPU-based computing would be an asset. The student should be motivated to work at the intersection of artificial intelligence, aging, and healthcare, and should be comfortable reading scientific literature and communicating technical findings.

Research area: I conduct research broadly in the field of human-computer interaction (HCI). I am interested in building innovative interactive systems and evaluating the user experience of technology. This covers both technologies that have permeated our daily lives (e.g., smartphones) as well as emerging technologies (e.g., virtual reality). Currently, I am particularly interested in cross-reality---the act of moving between realities, such as between virtual reality and physical reality.

Student roles:
The students will primarily act as developers with components of design and human-centered research included. They will have the following main responsibilities:
1) Build a system that enables users to integrate tangible object to be brought into VR and used them like in the real, physical world. Students can focus on a specific set of items that the systems will support.
2) Design and conduct an evaluation of the system with real users to gather feedback and initial impressions. This includes analyzing and reporting the results. The supervisor will work closely with the students when designing the study.

Skills required:
The students should have a background in computer science, software engineering, human-computer interaction, or similar field. They should have good programming skills, and preferably experience with computer vision and/or sensing technologies. Experience with virtual reality, human-centered design, and/or user studies is a significant advantage. The students must have good communication skills and the ability to work in a team.

Research area: We work at the intersection of machine learning, wearable sensors and software engineering for health applications extracting meaningful insights from continuous physiological and behavioral data. A core challenge we address is bridging the considerable gap between controlled laboratory environments and the complexities of real-life data. We study data variability, temporal patterns, and how individual differences in activity patterns or physiological responses impact data interpretation. We aim to design wearable health systems that are truly robust, reliable, and sustainable for widespread adoption, paving the way for truly personalized and preventable healthcare.

Student roles:
The interns’ primary responsibility is the implementation of RESTful API endpoints for data transformation, designing and implementing data transformation modules (including normalization, missing data imputation, segmentation, and feature extraction for accelerometer and heart rate data), and developing a robust suite of unit tests to ensure the reliability and accuracy of the API. They will actively participate in team meetings, contributing to design discussions and progress updates, and will contribute to the project’s technical documentation.
They will receive dedicated mentorship, including code reviews to guide their progress and learning. The lab will provide access to necessary datasets, an existing codebase for reference, and relevant scientific literature. Progress will be regularly assessed through weekly check-ins, code reviews, and a final presentation outlining their contributions and the functionality of the developed API.

Skills required:
strong programming skills, particularly in Python, or demonstrate a strong desire and commitment to rapidly acquire proficiency in the language. A basic understanding of machine learning concepts is desired. The most crucial attributes for this role include a high ability to learn quickly, a genuine passion for tackling challenging problems, a proactive approach to finding innovative solutions, and the capacity to collaborate effectively within a team environment. Prior experience with RESTful API development or data transformation pipelines is an asset

Research area: I specialize in sustainable digitalization by investigating how 1) technology contributes to sustainability; and 2) how technology can be made more sustainable such as by being more energy-efficient or by extending digital product lifetime to avoid waste from electrical and electronic equipment (WEEE), among others. I am a Tier-2 Canada Research Chair specializing in this emerging research area, which I aim to develop further as a research stream with both academic and managerial implications.

Student roles:
The research will be conducted in three phases where the student will be involved in:
- Step 1: collecting and synthesizing literature about the state of the art in sustainable digital.
- Step 2: Based on the literature review, develop a sustainable digital maturity self-assessment framework for organizations, adapt to small and medium-sized enterprises (SMEs) and contribute to the development of the platform hosting the index.
- Step 3: Develop a sustainable digitalization index based on the framework developed; ensure adaptation to SME; ideally, frame the preliminary content of the survey/questionnaire that will be used as basis for the index.
- Step 4: Collect longitudinal data from the sustainable digitalization index among companies; clean, organize and analyze data and interpret the findings.

Skills required:
The student has good reading and writing skills in English, particularly academic English (e.g., scientific articles, conference proceedings, book chapters, manuals, reports, working papers).
The student is hardworking and independent, yet comfortable working collaboratively when necessary.
Additionally, the preferred background is in computer engineering or information systems management, typically with strong conceptualization skills. Knowledge of marketing, management, and environmental sciences is an asset.
Indeed, a good understanding of business can be useful (marketing, finance, accounting, etc.).

Research area: My research focuses on the intersection of speech-language pathology and artificial intelligence, specifically the development of automated discourse analysis tools for aphasia rehabilitation. Discourse analysis, which is the analysis of connected language, is a critical but time-consuming clinical assessment. My work aims to make this process scalable by leveraging natural language processing and machine learning to automate the extraction of clinically meaningful discourse features. This research integrates perspectives from speech-language pathologists, stroke survivors, and their families to ensure the tool is clinically valid and practically usable.

Student roles:
This project hosts three student interns across two workstreams.

The first intern will contribute to the technical development of the AI-based discourse analysis tool. This student will work on building and iteratively testing the NLP pipeline responsible for automated extraction of discourse-level features from speech samples of people with aphasia. Tasks will include implementing and evaluating language models for feature extraction, processing speech and text data, and documenting the pipeline for future development. This work will directly inform the tool's clinician-facing interface.

The remaining two interns will contribute to the qualitative research workstream. These students will transcribe audio recordings from stakeholder interviews and focus groups conducted with speech-language pathologists, stroke survivors, and family caregivers. Following transcription, they will systematically analyze the data using thematic analysis to identify clinician and end-user requirements for the tool. Each intern will lead analysis of a distinct participant group: one focusing on speech-language pathologists, and one focusing on stroke survivors and family caregivers. Findings will be synthesized into co-designed tool specifications that feed directly into MVP development.

Skills required:
This project will host students with two distinct profiles. The first student should have a background in computer science, computational linguistics, or a related field, with experience in Python and natural language processing. Familiarity with large language models, speech and language processing pipelines, or machine learning is advantageous. The remaining two students should have a background in speech-language pathology, linguistics, rehabilitation sciences, health sciences, or a related field, with experience or strong interest in qualitative research methods. Familiarity with thematic or content analysis, interview-based research, and the aphasia population is advantageous for both.

Research area: The VR and Mobility lab is specialized in the understanding, evaluation and training of complex locomotor tasks in individuals with mobility disorders due to older age or following an acquired brain injury such as stroke or a traumatic brain injury. We use technology such as virtual and augmented reality, motion capture systems, EEG, EMG and more.

Student roles:
- Programming of scenes (environments) and basic functions in Unity or Unreal.
- Pilot testing of the developed application with a virtual reality headset during overground walking.
- Synchronization with other equipment, such as a motion capture system.

Skills required:
Basic knowledge in programming, and ideally a minimum of experience with either Unity or Unreal. The student should also have an interest for the field of physical rehabilitation and its applications.

Research area: My research focuses on the development of advanced neuroimaging tools and AI-powered analysis techniques to improve brain health assessment, particularly in pediatric populations. I design novel MRI pulse sequences and AI models for automated image analysis, enabling early detection and personalized interventions for conditions such as cerebral palsy, epilepsy, and autism spectrum disorders. My work bridges biomedical engineering, neuroscience, and clinical care, with an emphasis on translational research that creates impactful, real-world solutions for patients. We are highly interested in the use of AI agents to support workflows in our research program.

Student roles:
These internships are oriented towards the development of software that can be used towards the research program aims. The trainee will be required to develop software and commit their changes to a code repository. The hypotheses will be formulated in ongoing discussion with the supervisor and research team. The student is expected to meet for research team meetings, and one-to-one meetings with the supervisor weekly. We work in a dynamic team environment on many projects, so interacting with the other lab members is usually helpful.

The students have the opportunity to work with the more senior graduate students, and this will help them to get a better sense of the what a research career might have for them. There are many successful graduate students at the University of Calgary who have previously come on the Mitacs GlobalLink Program. It is a really great chance to make a small contribution to research, while learning a lot and visiting a new place.

Skills required:
The ideal student will have a strong background in biomedical engineering, computer science, or a related field. Essential skills include programming experience (e.g., Python, MATLAB) and familiarity with machine learning frameworks (e.g., PyTorch, TensorFlow). Knowledge of medical imaging (especially MRI) or signal processing is an asset. The student should demonstrate critical thinking, problem-solving abilities, and a strong interest in AI applications for healthcare. Prior experience with data analysis, model development, or research projects is desirable. The student should be a self-starter, capable of working independently and collaboratively in an interdisciplinary research environment.

Research area: My research specializes in intelligent condition monitoring and predictive maintenance of mechanical systems. I develop cost-effective techniques that integrate signal processing, machine learning, and dynamic modeling to detect early faults and assess the health of critical components such as gears, bearings, and rotating machinery. This work enhances system reliability, safety, and efficiency across industries including aerospace, transportation, and renewable energy.

Student roles:
The student will play a key role in designing and implementing AI agents for intelligent condition monitoring systems of mechanical components. Responsibilities include conducting literature reviews, collecting and analyzing vibration or acoustic data, developing signal processing and machine learning algorithms, and assisting in the development and testing of custom diagnostic software. The student will also be involved in lab experiments, documentation, and, where appropriate, contributing to research publications and presentations. Close collaboration with both academic and industry partners is expected throughout the project.

Skills required:
The ideal student should have a strong background in mechanical engineering, machine learning, or a related field. Key skills include proficiency in signal processing, programming (e.g., Python or MATLAB), and a solid understanding of dynamics and vibration analysis. Experience with machine learning, data analysis, or software development is highly desirable. The student should also be motivated, collaborative, and interested in applying advanced techniques to real-world engineering problems.

Research area: Currently, human cognition is currently experiencing cognitive decline, particularly with the emergence of AI, which encourages cognitive offloading. As a result, I am interested in Educational Technologies aimed at promoting learning and memory using AI and Natural Language Processing.

Student roles:
The student will be responsible for the following:
1. Carry out a literature review to understand the research problem and develop a solution
2. Develop and Evaluate web-based AI-Powered Mnemonic Generator and Cross-Lingual Mnemonic Converter
3. Work with graduate students to write and publish academic papers

Skills required:
1. Ability to program in Python and other languages
2. Experience working with Recurrent Neural Network and Deep Neural Network
3. Experience working with open-source AI models such as LLAMA
4. Experience in Natural Language Processing
5. Ability to build web applications

Research area: My research interest includes developing effective AI-driven solutions aimed to detect and avoid deceptive interfaces and/or AI slop to foster good user experience and digital trust in the online environment. My specialized research area for the proposed project is AI Quality Assessment, Human-Centered Computing, and Multimodal Content Analysis. This area focuses on developing computational methods to evaluate the quality, trustworthiness, and usability of AI-generated content across text, images, and videos. It combines techniques from machine learning, natural language processing, computer vision, and human-computer interaction to identify characteristics associated with shallow, low-quality, misleading, repetitive, or manipulative AI-generated outputs.

Student roles:
Carry out a literature review to understand the research problem and develop a solution
Design a taxonomy of AI slop
Develop automated AI slop detection tools (text, image, video)
Develop a unified AI Slop Index for content evaluation
Develop an open-source prototypes for research and integration into moderation systems
Test and create evaluation reports comparing model performance with human judgments
Work with graduate students to write and publish academic papers based on the evaluation results

Skills required:
Natural Language Processing
Computer Vision
Large Language Model (LLM)
Artificial Intelligence
Machine Learning
Convolutional Neural Networks
Multimodal Content Analysis

Research area: I am specialized in software engineering and generative AI

Student roles:
Systematic analysis of existing jailbreak techniques, development of new defense mechanisms against jailbreak attacks, running experiments to validate the proposed mechanisms, writing a research paper reporting the findings of the proposed project

Skills required:
Mastery of Python, basic knowledge on generative AI, creativity, excellent analytical skills, excellent interpersonal skills.

Research area: The Software Modeling and Simulation lab (SMS) is part of the software engineering group (GEODES). Our overall goal is to automate software tasks, promote reuse, and maximize the return of software development to increase user productivity. One of our research interests focuses on empirical software engineering. Empirical studies usually involve the collection and analysis of data through qualitative and quantitative methods that can be used to characterize, evaluate, and reveal relationships between practices and technologies. In particular, we are interested in developing software that helps conduct empirical studies on software programs, literature reviews, and other evidence-based sciences.

Student roles:
The intern will take-over the current version of ReLiS. He will be responsible for designing, implementing, and documenting the application. He will fix bugs identified by users and improve the code base. The intern will be involved in front-end and back-end development.
The work can be done in English or French.

Skills required:
The intern must have strong skills in programming, especially in web development. Programming languages: HTML5/JavaScript/CSS, PHP, MySQL, Python, and other third-party frameworks for web applications (e.g., code igniter, bootstrap). The intern will work in a team of undergraduate, masters, and doctoral students involved in the project. The intern is assumed to have basic knowledge in software engineering: bug tracking systems, version control systems, unit testing frameworks, object-oriented principles, and software design patterns. The ideal intern will have an eye for graphical user interface look-and-feel. An asset is to have experience with bibliography management tools, such as JabRef or EndNote.

Research area: My research area focuses on development and use of current technologies with applications to the scholarship of teaching and learning, sustainable development, problem solving, and intelligent systems using specialized algorithms for the purposes of automated control and artificial intelligence.

Student roles:
Working closely with the mentor, the student will help design a set of complex problems for the AI tools (such as CoPilot Agent) to solve. This will include a literature review on prompt generation, problem solving, and advanced use of AI tools. Rules for various strategies for solving the problems when issues arise will be developed. Each problem will then be broken down into the separate stages of the problem solving process and then solved using the AI tools. Resulting code will then be tested for accuracy and efficiency. Finally, a report will be written by the student based on the findings.

Skills required:
This project is well suited for students from computer science and engineering disciplines. Students should have a strong problem solving skill set and experience coding in various languages.

Research area: My specialized research interests lie in Software Engineering and Software Quality. Through my research, I aim to facilitate software development, maintenance, and evolution using machine learning and develop new methods and techniques. More specifically, I focus on analyzing software artifacts (e.g., design, source code, documentation, requirements, etc.) using machine learning and natural language processing techniques, assessing the quality of software design and implementation, analyzing and prioritizing software requirements, and managing technical debts or issues.

Student roles:
* Assist in developing and evaluating AI-based malware detection models
* Contribute to data collection, preprocessing, and experimental analysis
* Collaborate with team members through pair programming and shared research tasks
* Implement and test cybersecurity, explainability, and threat forecasting components
* Prepare technical documentation, reports, and research publication materials

Skills required:
* Strong programming skills in Python and machine learning frameworks such as PyTorch or TensorFlow
* Basic knowledge of cybersecurity, malware analysis, and software security concepts
* Familiarity with deep learning, AI, or large language model (LLM) techniques
* Experience with data analysis, experimentation, and collaborative development using Git
* Understanding of computer systems, networking, or low-level software representations such as APIs and syscalls

Research area: Our laboratory works on the development of personalized therapies in ovarian cancer. We employ longitudinal multi-omique analysis of clinical and pre-clinical samples to identify therapeutic vulnerabilities and therapeutic opportunities.

Student roles:
The student will be working with a database of single-cell proteomics and will build a new software that will be integrated to our cyclic-immunofluorescence platform.

Skills required:
Skills in coding is required. Python language knowledge is preferable.

Research area: Dr. Gueziri's research focuses on the design and evaluation of ultrasound-guided navigation systems in neurosurgery, the development of new smart sensor technologies, and the application of immersive visualization in medicine. This work encompasses three key research areas: surgical assistance, education and simulation, and the design of low-cost medical systems.

Student roles:
The intern will play a critical role in the development of a neurosurgical simulation platform for corticotomy. This project aims to enhance resident training by incorporating visual force feedback, providing a more realistic and accessible training alternative. The student must accomplish one of the two tasks.

Responsibilities:

Task 1:
Implementation of Image Registration Techniques:
- Develop and implement an image registration technique based on feature detection and tracking.
- Estimate local deformations from videos.
- Ensure accurate alignment of images to facilitate subsequent analysis.
- Implement algorithms for detecting and tracking features in video recordings.
- Validate and evaluate the effectiveness of the developed algorithms.

Task 2:
Development of a Biomechanical Model:
- Integrate a finite element method (FEM) model considering the properties of ex-vivo brain tissues.
- Collaborate in creating a biomechanical model that accurately represents brain tissue properties.
- Implement the FEM model to estimate the contact forces applied by surgical instruments.
- Ensure the model is compatible with the simulation platform and can provide accurate force estimations.
- Participate in the comparative evaluation between force estimations obtained through simulation and actual measurements captured by force sensors.
- Perform data analysis to assess the accuracy and reliability of the force estimations.

Skills required:
- Experience in Python programming.
- Strong organizational and methodical skills.
- Knowledge of computer vision and image processing is an asset.
- Interest in biomechanical simulations and neurosurgery.
- Ability to work in a team and collaborate with experts in neurosurgery and biomedical engineering.

Research area: Our group develops and tests novel health behaviour change interventions to reduce the development and progression of non-communicable chronic diseases. One of our key areas of interest is in the development of eHealth behaviour change platforms which incorporate behavioural theory, practical application, and advanced deep, machine, and reinforcement learning techniques. These platforms are developed to optimise the personalised experience that individuals have and through this, increase intrinsic motivation and self-efficacy (confidence) to change their behaviour. This collaborative work integrates medicine, computer science and engineering, psychology, and behavioural science, with input from community investigators and industry partners.

Student roles:
All students will be working with a strong team including expertise in all aspects of the project. As such, all facets of the work that they will be doing will be appropriately supervised.

On the engineering/computer science side, students will be required to develop and adapt system codes, as well as process and clean data. They will have the potential to work: with the behavioural science and computer science groups to refine pre-existing code; and on aspects of data integration between the multiple inputs to provide a connected real-time analysis and adaptation of the intervention.

On the behaviour change side, students will be required to adapt the intervention structure and components, as well as start to embedded experiments within the platform to explore unanswered questions. They will have the potential to work: with the behavioural science and computer science groups to refine the follow and structure of the intervention package; and on coding of the captured data to ensure that the appropriate triggers are driving appropriate personalisation components of the intervention.

All students are integrated into the broader team and have the capacity to input on the evolving research designs that are being used.

Skills required:
Students with either an engineering/computer science or behaviour change background are required for this project. For those with an engineering background prior experience with any of the following would be an asset: internet-mediated applications; IoT applications; and/or deep, machine, or reinforcement learning architectures. For those with a behaviour change/health science background prior experience with any of the following would be an asset: the development of a behaviour change intervention; coding of behaviour change techniques and/or intervention components; delivery of one to one counselling for behaviour change; training in motivational communication or interviewing; and/or ecological momentary assessment.

Research area: The laboratory’s research focuses on improving cancer immunotherapies, including both cellular and molecular therapies. Dr. This’s expertise combines high-throughput cellular imaging, computer-assisted image analysis, artificial intelligence approaches as well biophysical technologies for the manipulation of immune cells. The laboratory is particularly interested in T lymphocytes, with a central question: can their ability to eliminate tumor cells be predicted based on the dynamic analysis of their intracellular signaling? Thus, the laboratory is not only focused on addressing fundamental questions in tumor immunology, but also aims to develop cutting-edge microscopy and photonic tools to answer these questions.

Student roles:
Under the direct supervision of Dr. This, the intern will participate in the development of new experimental approaches in support of research projects. The intern will be required to develop image analyse methods for the prediction of cellular behavior from microscopy-based images.

Skills required:
Prior experience in bioinformatics is preferred. Knowledge in cell biology, photonics, or engineering is considered an asset. We are looking for curious and motivated students who are open to multidisciplinary learning and eager to develop new scientific and laboratory management skills. We strongly encourage applications from all qualified candidates, including those from marginalized communities.

Research area: Servier Virtual Cardiac Centre (SVCC) is an advanced technological laboratory dedicated to the development of innovative methods for reconstruction, post-processing and visualization of medical imaging for cardiac and non-cardiac applications. Medical and imaging research is a top priority at the University of Alberta and the Mazankowski Alberta Heart Institute, which makes the SVCC an ideal environment to foster anyone who wishes to pursue a career in medical imaging research. Our lab hosts a number of advanced visualization systems such as 3D stereo projector and IQ-station. Our lab also focuses on developing novel techniques for medical image processing.

Student roles:
The student will implement a volume rendering algorithm for Unity3D to load and display standard DICOM images. The volume rendering algorithm will be written in a general form so that the color and opacity values for image intensities can be set and edited by the user. A sequential rendering will be employed to represent animations such as a beating heart. The proposed application of Unity3D for rendering medical images requires a significant amount of programming. The student will spend a significant amount of time to implement essential software interaction tools for the Unity3D platform. The functionalities will include zoom, pan, rotate, multi-planar cropping, slicing, 3D distance measurement and angular measurement. The student will implement programs to assign these functionalities to the respective input devices such as Xbox game controller, leap motion, and 3D mouse. Extensive guidance will be provided with the development of the program.

Our lab is located inside the Mazankowski Alberta Heart Institute in Alberta, Canada. This offers an opportunity to regularly interact with cardiac radiologists, cardiologists and surgeons who will provide feedback on the development of the program. The student will get an opportunity to test his or her program on different types of hardware configurations and discuss the progress in weekly lab meetings.

Skills required:
The project requires programming skills in C# and Python. The successful candidate will be an undergraduate student from computer science, mathematics, electrical engineering, computer engineering or any other engineering discipline with a background in image processing & visualization, and computer graphics. Computer programming experience with image processing software packages such as Visualization Toolkit (VTK), Insight Segmentation and Registration Toolkit (ITK), and OpenCV is preferable. Programming experience with gaming engines such as Unity3D would be an asset.

Research area: This research operates at the intersection of e-privacy, cybersecurity, digital inclusion, and population aging. It examines how older adults across different countries navigate digital technologies, the privacy and security risks they face online, and the sociotechnical factors that shape their digital engagement. The project draws on harmonized longitudinal data from the Gateway to Global Aging Data (G2Aging) platform, supplemented by publicly available digital literacy and cybersecurity survey datasets, to develop cross-national insights and evidence-based policy recommendations.

Student roles:
The three interns will each lead one of the project’s three research streams while collaborating on cross-cutting deliverables. Intern 1 (Digital Engagement and Inclusion Analyst) will construct a cross-national dataset of digital engagement indicators for older adults using G2Aging harmonized data. This intern will perform descriptive and multivariate analyses examining how technology adoption, internet use, and digital service utilization among older adults vary by country, gender, education, income, and health status. The intern will produce comparative visualizations and a working paper on cross-national digital inclusion patterns. Intern 2 (Cybersecurity and Online Risk Analyst) will investigate the prevalence and determinants of cybersecurity incidents affecting older adults across multiple countries. This intern will integrate G2Aging data with supplementary datasets (e.g., national consumer fraud surveys, cybercrime victimization statistics) to model risk factors for online fraud, privacy breaches, and digital exploitation among older populations. The intern will develop a risk-factor framework and contribute to a policy brief on protecting older adults online. Intern 3 (Age-Friendly Privacy Interface Designer and Researcher) will synthesize findings from Streams 1 and 2 to develop evidence-based design principles for age-friendly digital privacy and security tools. Drawing on universal design, HCI, and usability literature, this intern will create annotated wireframes or low-fidelity prototypes illustrating age-sensitive privacy settings, security alerts, and consent interfaces. The intern will produce a design principles document and contribute to a conference paper. All three interns will participate in weekly supervisory meetings with Dr. Johnson, present at the research group seminar, and co-author shared deliverables. They will gain hands-on experience with real-world harmonized aging data, cross-national comparative research, cybersecurity analysis, and interdisciplinary collaboration.

Skills required:
Strong quantitative research skills, including proficiency in statistical software (R, Python, Stata, or SPSS). Experience with large-scale survey data analysis and data visualization. Familiarity with human–computer interaction (HCI) or user experience (UX) research methods is an asset for Stream 3. Understanding of cybersecurity fundamentals and/or digital privacy concepts. Knowledge of gerontology or aging-related research is a plus. Excellent English writing and communication skills.

Research area: Interdisciplinary areas of applied electromagnetics and wireless communications, with a particular focus on the development of high-performance computational models/algorithms for emerging wireless technologies in 5G/6G/THz wireless communications, intelligent transportation (air, ground, underground), underwater communications, industrial Internet of Things, as well as biomedical sensing and healthcare applications. Keywords: Applied Electromagnetics, 5G/6G Wireless, Integrated Sensing & Communication, Localization, Antennas & RF/Microwave Design, Machine Learning & Parametric Modeling, Stochastic Uncertainty Quantification, Internet of Things & Intelligent Systems.

Student roles:
(1) Assisting in conducting literature reviews to gather relevant research papers, articles, and other academic resources to support the research project.
(2) Collaborating with the research team to exchange ideas, share knowledge, and contribute to the overall research goals, including methodology & model development, results analysis, etc.
(3) Participating in group meetings, and effectively communicating project progress and results.
(4) Keeping records of research procedures, observations, and findings. Writing reports, summaries, and contributing to scientific papers writing or presentations.

Skills required:
The project suits students with interests and backgrounds in mathematics, electromagnetics or wireless communications, machine learning and programming.

Research area: My research focuses on privacy issues in mobile apps, especially considering the needs of people who lack technical knowledge of privacy and are particularly vulnerable. The main goal is to help non-experts stay protected against data theft and excessive data collection. This involves two main areas: 1. Understanding how nontechnical users reason about digital privacy, and to what the extent they are aware of privacy risks related to mobile data collection 2. Understanding how mobile app developers reason about the privacy of their users, and for what purpose and to what extent they choose to collect user data.

Student roles:
The student will join a research team that includes faculty members and graduate students. Here’s a detailed breakdown of the student’s activities.

In the initial learning phase (2-3 Weeks), the student will learn key concepts related to usable security, mobile app analysis, and mobile privacy. This will involve reading tutorials, blog posts, and scientific papers with guidance from the supervisor. The supervisor will answer questions, help the student learn new concepts, and provide simple exercises to support understanding.

In the design phase (4-6 Weeks), the student will assist a graduate student and the supervisor in designing user studies to gather information on how users and developers reason about privacy. This will entail participating in brainstorming ideas for interview questions and refining research questions; determining how to ensure data privacy, protecting participant data, and addressing ethics concerns; and helping create and submit an application for ethics committee approval. The student will receive joint support from more senior research team members during this phase.

In the interview pilot phase (3-5 Weeks), the student will help conduct pilot interviews with a small number of volunteers. This will involve assisting in asking interview questions, and helping to revise the study design and interview script based on feedback to ensure clarity. If time permits, the student will also assist in interviewing more participants after making necessary changes.

Throughout this project, the student will gain valuable experience in: designing and conducting user studies, conducting interviews, and analyzing qualitative data from interviews. It will also be introduced to how to effectively communicate privacy-relevant information to non-expert users. Thus, this project will provide a comprehensive learning experience in research design, data collection, and the practical aspects of digital privacy for nontechnical populations.

Skills required:
The student needs to have working understanding of privacy and security concepts: concepts such as digital privacy, data collection, dark patterns etc. The student also needs to have basic familiarity with usable security research: why it is important to study humans' role in security, what are the most common methods used to carry this kind of research.

Beyond that, the project just requires soft skills: curiosity about digital privacy issues, and the willingness to learn new concepts, skills, and tools. The advisor will be available to teach the student any concept that may be unfamiliar, should they need guidance.

Research area: My research focuses on the development of a Digital Twin (DT) framework for components fabricated through Additive Manufacturing (AM) techniques. A Digital Twin is a dynamic, virtual replica of a physical system that continuously mirrors its real-world counterpart through data integration. In this project, the physical system is the AM-produced part itself. DT serves as a powerful tool to detect potential faults, forecast performance deviations, and implement corrective actions in real time, which ultimately improves the reliability and quality of AM components.

Student roles:
The student will work with a team of other undergraduate and graduate students in carrying out the required tasks. The working hours are 40 per week. The student will also need to do a literature survey and summarize the status quo of the other methods with a thorough comparative analysis.
Finally, the student will summarize their findings, analyze their results, and prepare tables, graphs, or other documentation to present their research. The research progress is expected to be presented during group meetings.

Skills required:
The ideal candidate should have a background in computer engineering, mechanical engineering, computer science, or similar disciplines. Preference will be given to students with experience in game engine, machine learning, and computer vision. Training will be provided in these areas depending on the candidate's qualifications. The research involves both practical and theoretical work. The student must be hardworking, motivated, and able to work effectively in teams. Additionally, they should be comfortable presenting their findings to peers through reports and presentations.

Research area: This research area focuses on intelligent IoT systems enhanced by deep learning, reinforcement learning, federated learning, and emerging agentic AI. We study how distributed devices—such as Raspberry Pi and NVIDIA Jetson platforms—can perform on‑device inference and collaborative learning under real‑world constraints. The work spans Python‑based model development, edge deployment, and privacy‑preserving distributed optimization. Applications include smart environments, cyber‑physical systems, and adaptive automation, with emphasis on scalable, resource‑efficient, and privacy‑aware AI.

Student roles:
The student will contribute as a junior research developer working across software engineering, machine learning, and IoT experimentation. Responsibilities may include:
Software Development: Implement Python modules for data processing, model training, and experiment automation. Write clean, well‑structured code and use version control for reproducibility.
Model Training & Evaluation: Train deep learning and reinforcement learning models, run experiments, analyze metrics, and compare centralized, edge‑based, and federated training approaches.
Edge & Federated Learning: Assist in building federated learning workflows and deploy lightweight models on Raspberry Pi or NVIDIA Jetson devices to evaluate real‑world constraints.
Agentic AI Integration: Explore agentic components that automate tasks such as hyperparameter tuning, experiment scheduling, or adaptive system configuration.
IoT Data & Simulation: Work with sensor datasets and generate datasets or simulated IoT environments to test models under realistic conditions.
Research Collaboration: Participate in weekly discussions, propose ideas, refine hypotheses, and help shape experimental directions.
Documentation & Dissemination: Maintain detailed experiment logs, prepare technical summaries, and contribute to research outputs such as software repositories or draft publications.
This role provides hands‑on experience in modern AI systems, preparing the student for advanced research or industry careers in intelligent systems, distributed AI, and IoT innovation.

Skills required:
Strong Python programming skills and a foundational understanding of machine learning. Experience with NumPy, Pandas, or similar libraries is expected. Exposure to deep learning (TensorFlow or PyTorch), reinforcement learning, or IoT hardware is beneficial. Interest in federated learning, agentic AI, or edge devices (e.g., Raspberry Pi, NVIDIA Jetson) is an asset. The ideal student is analytical, motivated, and eager to learn new tools while contributing to applied research.

Research area: Software Programming, Cloud Computing, Embedded and real-time Systems, Internet of Things, Machine Learning

Student roles:
The student will be responsible for developing and integrating the infrastructure components of the IoT–edge platform. Activities will include configuring embedded devices, connecting sensors or cameras, deploying lightweight AI workloads, and implementing communication mechanisms between IoT nodes and edge servers.

The student will design and implement device-side software capable of collecting data, monitoring resource utilization, and interacting with edge services. Resource monitoring may include CPU usage, memory utilization, communication quality, inference latency, and energy-related metrics.

On the edge side, the student will configure edge servers and develop orchestration services capable of receiving requests from multiple devices, managing task execution, and coordinating workload distribution. The student will implement communication protocols, APIs, or messaging services supporting task offloading and result delivery.
A significant part of the work will involve system integration and performance evaluation. The student will design experiments to characterize latency, throughput, bandwidth utilization, resource consumption, and scalability under different deployment scenarios.
Expected outcomes include a functional IoT–edge platform, deployment and configuration scripts, communication and orchestration modules, monitoring tools, experimental reports, and technical documentation describing the system architecture and evaluation results.

Skills required:
Python and/or C/C++ programming
Linux development environment
Git and software version control
Embedded systems and Raspberry Pi/Jetson platforms
Computer networks and client-server communication
REST APIs, sockets, or messaging systems
Docker and containerized deployment (asset)
System monitoring and performance evaluation
Debugging and troubleshooting skills
Technical documentation and experimentation

Research area: Software Engineering,AI,Machine Learning,Programming Languages

Student roles:
The student is responsible for designing and implementing components of a Distributed Job Failure Tracker, including failure detection, logging, and centralized tracking of job execution errors. The role also involves analyzing failure patterns, supporting testing, and contributing to basic reporting or visualization of system failures.

Skills required:
Should be an undergraduate CS student in their final year and have knowledge in programming in python

Research area: My research group at UBC studies the Internet through large-scale empirical measurement, with a focus on online privacy, security, and trust. We investigate whether the technologies and policies that increasingly shape how people access the Internet, from encrypted protocols and DNS filtering to legally mandated controls such as online age verification, actually behave as intended in practice. Our work combines network measurement, traffic analysis, browser automation, and analysis of legal and policy texts to produce independent, reproducible evidence for operators, regulators, and the public. The goal is rigorous measurement that supports an open, trustworthy Internet and informs evidence-based policy.

Student roles:
The intern will be a hands-on contributor to an active research project and will own a well-scoped piece of it from start to finish. Their core responsibilities will be:

+ Ramp-up (weeks 1-2): read the group's existing age-verification study and a small set of key papers, set up the development environment, and learn the group's browser-automation and traffic-capture tools.

+ Building the harness (weeks 3-7): implement a reproducible browser-automation pipeline that drives each platform's age-verification flow using synthetic, disposable personas, and capture the network metadata generated during each run. Extend coverage to one or more new jurisdictions, with a focus on the European Union.

+ Measurement and analysis (weeks 6-10): use the captured data to map the third-party age-assurance vendors involved, the categories of data exchanged, and where that data is routed; code each jurisdiction's legal requirements against the behavior actually observed; and test documented bypasses. The intern will produce clear figures and tables summarizing the findings.

+ Write-up and presentation (weeks 10-12): document the methodology, results, and limitations in a short technical report, and present the work to the research group.

Throughout, the intern will meet weekly with the supervisor for guidance, participate in group meetings, and work alongside graduate students who provide day-to-day mentorship. The intern is expected to use only synthetic test data, follow the group's responsible-research and ethics practices, keep reproducible records of experiments, write clean and documented code, and raise questions early. The role suits a motivated undergraduate who wants a genuine research experience at the intersection of measurement, privacy, and technology policy, and the chance to make a measurable contribution. A core deliverable plus optional stretch goals ensures a meaningful result, and exceptional work may lead to co-authorship on a publication.

Skills required:
Applicants should have a solid programming foundation, ideally in Python, and be comfortable working in a Linux/command-line environment and with version control (Git). Coursework or project experience in at least one of the following is expected: web technologies (HTTP, TLS, DNS), browser automation, data analysis, security and privacy, or natural language processing. Familiarity with analyzing datasets (for example with pandas) is valuable. An asset, but not required: the ability to read legal, policy, or platform text in French or another European language, and an interest in technology policy. Most important are curiosity, rigor, attention to detail, and ethical maturity.

Research area: My broad area of interest is Software Engineering, with a growing focus on Artificial Intelligence (AI) and its applications. In particular, I am interested in AI for Software Engineering, the use of AI in Electric Vehicles (EVs), and intelligent systems for Autonomous Vehicle Scheduling. My research interests also include Model-Driven Software Engineering, Software Architecture, and Mining Software Repositories. I am particularly interested in leveraging AI techniques to address complex software engineering challenges and to develop innovative solutions in emerging domains such as autonomous and electric mobility.

Student roles:
The successful candidate will be involved in software development and programming, designing and conducting experiments, analyzing results, and contributing to the preparation of scientific reports, technical documents, and research publications.

Skills required:
Required qualifications include programming skills in at least one programming language, preferably Python. Applicants should have some knowledge of and background in advanced calculus. Prior coursework, experience, or a background in Artificial Intelligence (AI) is preferred.

Research area: My lab studies human state and social interaction using synchronized EEG, eye tracking, facial video, movement, and physiological sensors. We develop computational methods for linking brain rhythms, gaze, arousal, facial expression, posture, and behavior across individuals during realistic social situations.

Student roles:
Two interns will divide the project into experimental synchronization and analysis streams. Intern 1 will focus on protocol execution: participant setup, dual-device calibration, event markers, synchronization tests, scenario timing, and data-quality logs. Intern 2 will focus on analysis: aligning two-person recordings, extracting gaze/facial/physiology/EEG features, computing coupling metrics, and building classification models for social condition. Both interns will help refine the social scenarios, pilot the hardware, write documentation, and prepare a reproducible dataset structure. Final deliverables should include synchronized sample recordings, a working analysis notebook, quality-control plots, and a brief report identifying which signals best capture interpersonal coupling.

Skills required:
Students should have Python programming experience and interest in neuroscience, social interaction, machine learning, signal processing, or human-computer interaction. Helpful background includes time-series analysis, statistics, EEG/eye tracking, computer vision, experimental psychology, or hardware synchronization. Students must be careful, organized, and comfortable assisting with human-participant recordings.

Research area: My substantive research interests lie in digital marketing, with a particular focus on the creator economy, influencer marketing, and livestream shopping. I study how firms, platforms, and influencers interact in environments characterized by externalities and dynamic decision-making. Methodologically, I use analytical modeling, causal inference, and machine learning to examine influencer value, brand–influencer collaboration, the role of followers and influencers' timing strategies.

Student roles:
The student will support the project by assisting with data collection, data cleaning, data visualization, and preliminary analysis of digital trace data related to influencer marketing, livestream commerce, and brand–influencer partnerships. The student will help collect and organize datasets at different unit of analysis level( i.e., influencer, product, or campaign); construct key variables related to influencer characteristics, audience engagement, promotional activity, partnership structure, compensation arrangements, and performance outcomes; produce descriptive statistics; and prepare summary tables and figures. In addition, the student will have the opportunity to synthesize literature in relevant areas, including influencer marketing, social media marketing, e-commerce, livestream commerce, and digital platform markets.

Depending on project progress, the student may assist with basic regression analysis or machine learning applications under the supervision of the faculty researcher. The student will also document data-processing procedures and summarize analytical findings for research reports, presentations, or partner-facing outputs. Through this role, the student will gain training in digital marketing analytics, empirical research methods, and the analysis of brand–influencer collaborations.

Skills required:
The ideal intern should have strong quantitative research skills and experience working with large-scale datasets. Proficiency in Python, R, or Stata is required. The intern should be comfortable with data collection, data cleaning, statistical descriptive analysis, and regression-based methods. Strong attention to detail, clear documentation of data-processing and analytical steps, and the ability to work independently are also essential. Familiarity with mathematical modelling, causal inference, machine learning, natural language processing, or web scraping would be an asset. Prior knowledge of research on social media marketing, influencer marketing, e-commerce, or livestream shopping is preferred but not required.

Research area: My research focuses on Responsible AI, specifically making sure that machine learning systems are fair, transparent, and trustworthy when used in important areas like healthcare, criminal justice, and finance. When AI is used to make life-changing decisions, it isn't enough for a model to just be accurate; we also need to understand why it makes certain decisions (explainability) and ensure it treats everyone equitably (fairness). My goal is to build practical tools that prevent AI from amplifying societal biases, making complex algorithms easier to trust, audit, and safely deploy in the real world.

Student roles:
The student will participate mainly in the implementation of the proposed approach. Based on the initial prototype, the student will acquire enough knowledge to be able to also brainstorm and work on improving the initial proposed solution and its evaluation.

Skills required:
Programming (Python) + Basic knowledge of machine learning algorithms.
Understanding fairness and explainability is a plus but not a requirement.

Research area: My research interests are in supply chain analytics, logistics and transportation science with methodological interests in large-scale optimization, optimization under uncertainty, network optimization, and data-driven decision making. In my research, I develop innovative analytical models, optimization methodologies, and decision support tools for solving complex strategic design, tactical planning, and operational decision problems arising in following areas: Supply Chains; Logistics and Transportation; Supply Chain Resilience and Risk Management; Omnichannel Retail Analytics; Decarbonization of Supply Chain and Logistics Systems; Network Interdiction and Fortification. For publications, please refer to my google scholar page.

Student roles:
Successful students are typically motivated to conduct research at the intersection of optimization, supply chain management, transportation systems, and business analytics. They should be interested in developing mathematical models, designing exact and heuristic solution methods, and translating analytical insights into practical decision-support tools. Strong communication skills, the ability to read and critically assess academic literature, and a willingness to work independently while collaborating effectively with other researchers are essential.
Experience with programming languages such as Python, C++, or Java, as well as familiarity with optimization modeling environments and commercial solvers such as Gurobi, CPLEX, or Xpress is need. The student need to know C plus plus programming. It is OK if you are proficient in another programming language, but you definitely need to learn C plus plus for this research. In this case. Experience in calling GUROBI and/or cplex in C plus plus in a big plus.

Skills required:
Students interested in working on this project should have a strong quantitative background in Industrial Engineering, Operations Research, Supply Chain Management, Business Analytics, Applied Mathematics, Computer Science. A solid foundation in mathematical optimization is particularly important, including knowledge of linear programming, integer and mixed-integer programming, network optimization, and stochastic modeling. Experience with programming languages such as Python, C++, or Java, as well as familiarity with optimization modeling environments and commercial solvers such as Gurobi, CPLEX, or Xpress, is highly desirable.

Research area: This research lies at the intersection of robotics, artificial intelligence, and design. A central challenge in robot learning is the lack of benchmarks where latency-aware decision-making is critical; most existing benchmarks evaluate robots in static environments that do not capture the timing constraints encountered during real-world task execution. The lab develops modular physical taskboards, simulation testbeds, and latency-aware evaluation frameworks to study how policy inference time and environmental dynamics affect robotic performance and generalization, supported by a four-arm robot platform and multi-camera depth-sensing infrastructure for physical validation.

Student roles:
The student will contribute across the full development cycle of a novel benchmarking platform for robot learning under real-time constraints, spanning literature review, design, simulation, machine learning experiments, fabrication, and physical testing.

The internship will begin with a brief literature review of existing robotic assembly benchmarks and dynamic manufacturing tasks, establishing the research context and informing design decisions. The student will then design modular taskboard components using CAD software, with emphasis on dynamic elements such as moving fixtures, time-sensitive constraints, and configurable task parameters suited for both simulation import and physical fabrication.

Working with the MuJoCo simulation framework, the student will import CAD models, configure task dynamics, validate simulation behavior, and generate task variants spanning a range of timing conditions and difficulty levels. This simulation environment will serve as the pilot platform for controlled robot learning experiments.

Working alongside graduate researchers, the student will assist in collecting demonstration trajectories and preparing datasets for robot learning experiments. The student will participate in training and evaluating robot learning policies, including analysis of task success rates, inference latency, and the relationship between computational delays and task performance.

The student will also contribute to fabricating selected taskboard components using Makerspace resources and integrating the physical hardware into the laboratory's robot learning infrastructure. Physical validation experiments using teleoperation and multi-camera sensing systems will be conducted to compare physical and simulated task performance.

Throughout the internship, the student will gain hands-on experience in engineering design, robot learning, additive manufacturing, experimental data collection, and performance analysis. Deliverables include taskboard CAD designs, MuJoCo simulation environments, demonstration datasets, evaluation reports, and contributions to conference presentations or posters summarizing project outcomes.

Skills required:
The ideal candidate must have strong analytical skills and problem-soving abilities along with creativity. The candidate must be open to work within a multidisciplinary team. Ideally, the candidate is enrolled in mechanical engineering, mechatronics, software engineering, electrical engineering, computer science, or a closely related discipline. Proficiency in Python and experience with CAD software (SolidWorks, Fusion 360, or equivalent) are desirable. Familiarity with robotics, simulation environments, computer vision, and machine learning is an asset but not required.

Research area: My research focuses on the reliable and robust integration of AI components, including large language models and multi-agent systems, into software engineering processes. I develop techniques that bring engineering rigor to AI-assisted development through two main approaches: model-based validation, which uses formal models and constraints to provide correctness guarantees and fallback mechanisms for AI-generated outputs; and principled agentic workflow design, which structures LLM-driven workflows to behave predictably and verifiably. I am also broadly interested in empirically evaluating the quality of ML models on tasks such as code generation, bug detection, and code summarization.

Student roles:
Working together with the supervisor and other maintainers of Sherpa, the student will help design, implement, and evaluate approaches for dynamic workflow generation within the framework. This includes reviewing relevant work on LLM-based agents and structured workflow design, developing methods that allow an LLM to construct, extend, or repair structured workflows at runtime, and integrating these methods into the Sherpa codebase. The student will evaluate the generated workflows in terms of task success, reliability, and robustness across different LLMs, analyze the results, and contribute code, tests, and documentation back to the project. They will also participate in regular research meetings and present their progress.

Skills required:
The student should have solid Python programming experience, including the ability to read and contribute to an existing codebase, along with foundational knowledge of machine learning and large language models. Familiarity with LLM-based agents or agentic frameworks (such as Sherpa or LangGraph) is an asset, as is exposure to state machines or workflow modeling. Comfort with software engineering practices such as Git, testing, and collaborative open-source development is expected.

Research area: Professeure en informatique de la santé à la faculté de médecine et des sciences de la santé et à la faculté des sciences de l’Université de Sherbrooke. Je m’intéresse à la modélisation de bases de données temporelles, les langages de définition et d’interrogation de bases de données à l’aide d’ontologies, l’analyse et le raisonnement temporels, la génération de graphes de connaissances et l’intégration de données pour les systèmes de santé apprenants.

Student roles:
Le projet consiste à :
• Développer une interface personne-machine intégrant des méthodes de visualisation
interactive pour faciliter la navigation dans les ontologies;
• Définir des fonctions de paramétrages de requêtes;
• Définir les scénarios d’essais (jeux de données, les requêtes et les transactions) ;
• Évaluer le composant avec des scénarios de test.

Skills required:
Connaissances cibles :
• Conception et développement d’interface personne-machine (UI)
• Bases de données de graphes
• Ontologies

Technologies :
• Java, TypeScript, AngularJS
• PostgreSQL, Neo4J
• Asciidoc, Gradle, Git/Gitlab
• IntelliJ

Research area: My specialized research area focuses on leveraging non-invasive brain signals, specifically electroencephalography (EEG), magnetoencephalography (MEG), and both structural and functional magnetic resonance imaging (MRI), to understand brain function and dysfunction. I investigate how variability in these neurophysiological and neuroanatomical markers relates to brain pathology in neurological and neuropsychiatric populations, as well as how they map onto typical brain development and the ageing process. A core aim of my research program is to unravel the functional and cognitive implications of these signal variations, ultimately seeking to enhance our understanding, detection capabilities, and therapeutic approaches for brain disorders.

Student roles:
The ideal candidate for this internship will possess a strong academic background in a quantitative discipline. This typically includes fields such as Mathematics, Statistics, Computer Science, Physics, Engineering, or a closely related area with a significant analytical component, providing the foundational knowledge necessary for complex data analysis and modeling.
Familiarity with the Python programming language is beneficial, as it will be the primary tool for data manipulation, analysis, and algorithm implementation within this project.
Furthermore, the candidate should have a foundational understanding of statistical principles and techniques. Prior experience applying machine learning concepts, even in academic coursework or personal projects, would be highly advantageous.

Skills required:
The student intern's primary responsibility will be to conduct a rigorous analysis of the associations between solar-driven geomagnetic disturbance (GMD) indices and electroencephalogram (EEG) data from an extensive clinical database. This will involve employing advanced statistical techniques (largely in Python), including predictive analytics and machine learning models, to identify correlations and potential predictive relationships. Key tasks include data preprocessing, feature extraction, model development, and validation of analytical pipelines.
A significant component of this role involves disseminating research outcomes. The intern will be expected to contribute substantially to, and potentially lead, the preparation of a research manuscript suitable for peer-reviewed publication.

Research area: I am a recognized expert in signal processing, machine learning, and Al, as applied to biomedical engineering. My lab conducts broad research across areas of human movement, health, and happiness, including topics like human-machine interaction (e.g. prostheses, robots, VR/AR, etc.), rehabilitation engineering (e.g. spinal cord injury, stroke, and Parkinson's disease), gait and mobility monitoring (for both health and security).

Student roles:
The successful candidate will join a multidisciplinary team, comprised mostly of computer scientists, engineers, and clinicians. They will work with other students, ranging from undergraduate level to PhD, to postdoctoral fellows and Professors. They will become an active and important member of the Institute, contributing to 1) their own line of research (to be identified in collaboration with Dr. Scheme on or in anticipation of arrival), 2) the progress of the research team working on gait biometrics, and 3) the overall atmosphere at the Institute of Biomedical Engineering (including research jams - like hackathons, but for research and team building, social outings, journals clubs, and lunch breaks). The student will be encouraged to pursue publication of strong work and results stemming from their own line of research and/or from collaborations with graduate students.

The student will be expected to arrive with an open and curious mindset. They will be asked to review the relevant literature, work with the team to identify a clear and actionable research gap to work on for the summer, manage their time, attend weekly lab meetings to review and present progress, collaborate with others, and esnure that the results of their work (including research findings, results, code, and documentation) are effectively translated back to the team before they leave.

Skills required:
The candidate should have demonstrated experience in machine learning, Al and/or signal processing, preferably with a strong background in Python. A background in biomedical engineering and/or human-computer interaction is an asset, and experience in biosignals like EMG is a bonus. The student must be able to work as part of a team, but be able to work independently and manage their time between meetings.

Research area: Wassim Bouachir is a professor of computer science at TÉLUQ (University of Québec). His research work focuses on the development of novel machine learning and computer vision methods for a wide range of applications, such as security, environment sciences, and health-care systems.

Student roles:
The student will play a crucial role in the research project. His/her main objective will be to improve the performance of the automatic detection algorithm for concerning behaviors in the context of intelligent video surveillance. He/she will work on enhancing the supervised machine learning model, which will involve tasks such as:
1- Data preparation: The student will prepare the necessary data for training and evaluating the model. This may include annotating existing videos with relevant concerning behaviors.
2- Algorithm development: The student will design and implement improvements to the automatic detection algorithm using machine learning and computer vision techniques. This may involve exploring different neural network architectures, optimizing hyperparameters, and utilizing advanced supervised learning methods.
3- Collaboration and communication: The student will work closely with other members of the research team, including experts in computer vision. They will need to regularly communicate their progress, share their findings, and discuss any challenges encountered.

Skills required:
The programming tasks will involve the use of Python and machine learning and image processing libraries such as Keras, PyTorch, OpenCV, scikit-image, and TensorFlow. It is not necessary to have prior mastery of all these technologies. However, it is important to have good Python programming skills and basic knowledge of machine learning. An understanding of deep learning-based object detection techniques like YOLO, as well as familiarity with recurrent neural networks (RNNs), would be advantageous.

Research area: Dr. Samar Garrab is an Assistant Professor in the Department of Management and Chair of the MBA program at the Royal Military College of Canada. She holds a PhD in Business Administration (specialization in Decision Sciences) and master's and bachelor's degrees in Business Computing. Her research focuses on sustainable development, artificial intelligence, green computing, optimization, and decision analysis.

Student roles:
The intern will contribute to the project by performing the following tasks:
1. Study the existing literature on green artificial intelligence algorithms
2. Evaluate the energy efficiency and resilience of given AI models (coding)
3. Enhance artificial intelligence algorithms to greener and more resilient versions (coding)

Skills required:
We welcome applications from motivated undergraduate and master’s students interested in artificial intelligence, especially deep learning and computer vision.
Applicants should be comfortable with programming (preferably in Python) and have some exposure to tools such as PyTorch or TensorFlow.
A basic understanding of machine learning, statistical analysis, and data analysis is expected. Previous project experience is a plus but not required. We are looking for curious, proactive students who enjoy solving problems and learning new skills. An interest in sustainability and in applying AI to real-world challenges, such as healthcare or environmental systems, is highly valued.

Research area: Artificial Intelligence in Education Educational Data Mining Affective Computing Data Visualization Machine Learning Neural Networks and Deep Learning Incremental Learning Image Processing and Computer Vision

Student roles:
Designing and developing system to apply in distance education
Reviewing research articles, and implementing and analyzing systems for performance evaluation
Writing report and research articles
Doing project presentations in the progress meeting

Skills required:
Programming experiences and skills in Matlab or Python
Experience and ability to design and build tools or systems
Experience in designing user interfaces and data visualization
Student must have knowledge in image processing, computer vision, machine learning, and pattern classification.
Experience in information collection, re-organization, summarization, and preparing reports.
Good communication skills.

Research area: My research focuses on advanced power system protection, monitoring, and modeling for modern grids with high penetration of inverter-based resources and large loads such as data centers. I develop both physics-based and data-driven methodologies, including waveform-based analytics and optimization-based parameter estimation, to enable reliable protective relaying, system monitoring, and high-fidelity modeling. My research integrates real-time protection & monitoring and offline simulation (e.g., EMT and stability studies) to address emerging challenges in low-inertia, inverter-dominated power systems. With prior industry experience at utilities and system operators, my research emphasizes practical, deployable solutions aligned with real-world grid operations and planning needs.

Student roles:
This project is planned to train two students. Their roles are described as follows.
Student 1 – EMT Simulation, PSCAD Automation, and Dataset Development
The first student will focus on PSCAD simulation. The primary responsibility is to build and validate simulation cases representing AI data center load behavior. This includes modeling AI data centers and generating both compliant and non-compliant ramping scenarios. A key task is to develop Python-based automation scripts to control PSCAD simulations, enabling large-scale, repeatable data generation. This includes automated case setup, waveform extraction, and structured data labeling. The student will ensure quality of generated waveforms and support dataset structuring for downstream machine learning applications. The deliverable is a high-quality dataset of approximately 5,000 labeled ramp scenarios suitable for Edge AI model training. This role is primarily simulation- and programming-intensive, training strong EMT simulation skills and coding ability for simulation automation and data pipeline development.

Student 2 – Edge AI Algorithm Development and Validation
The second student will focus on development of Edge AI algorithms for real-time ramp rate compliance monitoring using waveform data. The primary responsibility is to design lightweight AI models capable of detecting and classifying fast ramp events using electrical waveforms. The student will develop and benchmark AI models with emphasis on ultra-low latency inference and robustness under varying grid conditions. The student will also contribute to implementing a short-horizon predictive capability for early identification of fast ramp events and assess its effectiveness for preventive control applications. This role is primarily AI- and data-driven, bridging power system signals with machine learning methods to enable grid-edge monitoring solutions.

Collaboration Between Students
Both students will collaborate closely to integrate the developed AI algorithms into a unified workflow and evaluate system performance in terms of detection accuracy, false alarm rate, and detection latency under realistic simulated grid.

Skills required:
Strong background in power systems with a solid understanding of grid operation, load modeling, and protective relaying. Proficiency in Python is required for data processing and development of machine learning algorithms, particularly for time-series analysis and waveform-based classification. Familiarity with signal processing techniques and interpretation of electrical waveforms is important. Knowledge of AI data center load modeling is highly desirable. Experience with EMT simulation (e.g., PSCAD, Simulink) is an asset. The student should be able to connect power system physics with data-driven methods and work effectively on simulation-based research and algorithm development.

Research area: My research focuses on advanced power system protection, monitoring, and modeling for modern grids with high penetration of inverter-based resources and large loads such as data centers. I develop both physics-based and data-driven methodologies, including waveform-based analytics and optimization-based parameter estimation, to enable reliable protective relaying, system monitoring, and high-fidelity modeling. My research integrates real-time protection & monitoring and offline simulation (e.g., EMT and RMS) to address emerging challenges in low-inertia, inverter-dominated power systems. With prior industry experience at utilities and system operators, my research emphasizes practical, deployable solutions aligned with real-world grid operations and planning needs.

Student roles:
The student will focus on the development and evaluation of edge-enabled, data-driven anti-islanding detection methods for distributed energy resources (DERs), leveraging existing PSCAD-based simulation models developed by the research group and real-world measurement data. The role is applied and centers on lightweight AI algorithm development for real-time anti-islanding detection.

The student will begin by working with pre-generated simulation datasets representing a range of operating conditions, including challenging scenarios such as close load–generation matching. Using these datasets, the student will analyze electrical signals (e.g., voltage and current waveform characteristics) to identify features that distinguish islanding from normal operation.

Building on this analysis, the student will design and implement computationally efficient detection methods suitable for edge deployment. This may include feature-based approaches, threshold logic, and lightweight machine learning algorithms (e.g., classification or anomaly detection) implemented in Python. A key emphasis will be on “frugal AI,” ensuring that the methods are robust, interpretable, and viable under limited data and computational constraints.

The student will evaluate algorithm performance across multiple scenarios, assessing detection accuracy, robustness to noise, and sensitivity to system conditions. The work will also consider practical aspects such as data requirements and implementation feasibility in real-world utility environments.

The student will participate in regular meetings with the research team and may engage with an industry partner to understand operational constraints and ensure practical relevance. The student will document results, contribute to technical reports and conference/journal papers, and present findings at the conclusion of the project.

Through this role, the student will gain hands-on experience in applied AI for power systems, signal processing, and real-world engineering problem-solving in modern grid applications.

Skills required:
The ideal student will have a background in electrical engineering, with foundational knowledge of power systems (e.g., power system analysis, or protection). Familiarity with basic programming (e.g., Python or MATLAB) is required. Exposure to signals and systems, control, or machine learning concepts is an asset but not mandatory. The student should be comfortable working with technical software tools and willing to learn power system simulation platforms (e.g., EMT-type tools). Strong analytical thinking, problem-solving ability, and attention to detail are important. The project is well-suited for motivated students interested in modern power systems, distributed energy resources, and applied AI.

Research area: My research focuses on data structures and algorithms, with a specific emphasis on strings and combinatorics on words, with applications in bioinformatics and information retrieval.

Student roles:
The prospective undergraduate student will play an active role in both the theoretical and computational aspects of the project. They will study relevant literature related to the project topic, with a particular focus on existing data structures (such as suffix trees, suffix arrays, and related structures) and their applications. Building on this foundation, the student will contribute to the development of time- and space-efficient solutions aimed at improving existing approaches to the kkk-tuple Common Substring problem, and will implement these solutions in C++ and benchmark their performance. The student will also write Python scripts to generate random texts with specific properties, such as periodicity and repetitions, for use during benchmarking.

Skills required:
Excellent programming skills in Python and C++, Excellent data structures and algorithms, and complexity theory knowledge

Research area: My research focuses on developing efficient learning methods that reduce the need for supervision. The objective is to leverage the exponentially growing amount of data generated by the internet and connected systems (e.g., social media, Internet of Things) to build more capable deep learning models while reducing both their computational requirements and dependence on costly human annotations. My work is primarily applied to visual recognition and understanding tasks involving images and videos.

Student roles:
The student will contribute to the development, implementation, and evaluation of algorithms related to the proposed project. To support this work, the student will conduct a literature review of relevant scientific publications to gain a comprehensive understanding of the state of the art in the field.

The student will learn to use and efficiently share the computational resources available in the laboratory, including GPU servers and software infrastructure, in order to perform large-scale experiments. They will be responsible for designing experiments, analyzing results, and assessing the performance of the proposed methods.

The student will also be expected to document their work and disseminate the results through technical reports and, when appropriate, scientific publications.

The student will be integrated into a dynamic research environment and hosted within my laboratory, where they will interact with MSc and PhD students working on related topics in machine learning and computer vision. The student will receive regular supervision from me and will be paired with a more experienced graduate student who will provide day-to-day guidance and technical support throughout the project.

In addition, the student will have the opportunity to participate in the laboratory's regular activities, including reading groups, research seminars, invited talks, and other scientific and social events, providing valuable exposure to current research trends and fostering professional development.

Skills required:
The ideal candidate will have a strong mathematical background, solid programming skills (Python/PyTorch), and prior experience with Deep Learning and Computer Vision. The student should be comfortable developing and implementing algorithms, analyzing experimental results, and collaborating with other students as part of a multidisciplinary research team.

Research area: My research interests span various areas in systems software and distributed systems. I am interested in designing large-scale software systems, with a focus on improving the performance, scalability and reliability of these systems.

Student roles:
In this project, the student will start by running experiments and evaluating the performance of our streaming system. Currently, our streaming system is based on Apache Flink 1.0. The student will implement our system on Apache Flink 2.0 that supports various new features such disaggregated storage, data prefetching and efficient reconfiguration.

Skills required:
- Strong expertise in C and C++
- Taken course in operating systems
- Basic knowledge of the internals of the Linux operating system
- Knowledge of scripting languages such as Python
- Worked with source code tools such as Git

- Preferably taken courses in databases and distributed systems
- Preferably worked on some open-source projects

Research area: My research focuses on Sustainable Artificial Intelligence (Sustainable AI), with an emphasis on developing efficient, scalable, and environmentally responsible AI systems. As modern AI models continue to grow in size and computational requirements, my work explores methods to reduce their energy consumption, carbon footprint, and infrastructure costs while maintaining high performance and reliability. In addition, I am interested in Large Language Model (LLM) optimization and efficiency, particularly in reducing model complexity and designing lightweight architectures that can be deployed in resource-constrained environments.

Student roles:
The student will participate in the design, implementation, and evaluation of model splitting strategies for containerized Large Language Model (LLM) deployment. Their responsibilities will include studying the architecture of transformer-based models, identifying potential partition points within the model, and implementing prototype deployments where model segments are executed across multiple containers. The student will develop and configure experimental environments using tools such as Docker and Python-based machine learning frameworks, while ensuring efficient communication of intermediate tensors between model partitions.
In addition, the student will design and conduct benchmarking experiments to measure communication overhead, inference latency, throughput, memory utilization, and computational load distribution under different deployment scenarios. They will analyze the collected performance data, compare alternative partitioning strategies, and document their findings. As part of the research component of the project, the student will also work on research publications by conducting literature reviews, analyzing experimental results, and contributing to the writing and presentation of research findings.

Skills required:
Students interested in this project should have a strong foundation in computer science, software engineering, computer engineering, or a related field. Familiarity with machine learning and deep learning concepts, particularly neural networks and transformer-based architectures, is highly desirable. Basic knowledge of Large Language Models (LLMs) and inference workflows will be beneficial but is not mandatory. Students should possess programming experience in Python and be comfortable working with machine learning frameworks such as PyTorch. Knowledge of Docker containers, Linux environments, and distributed systems concepts is recommended, as the project involves deploying model components across multiple containers.

Research area: My research focuses on efficient and sustainable computing systems for large language models (LLMs). I study how to reduce the latency, energy consumption, and operational cost of LLM inference through intelligent resource management. Key topics include GPU scheduling, memory and cache management, workload prediction, and adaptive model serving. The broader goal is to develop practical systems that allow AI services to support more users with fewer computing resources while meeting strict response-time requirements. We use methods from machine learning, mathematical optimization, and AI systems.

Student roles:
The student will work closely with our research team of graduate students and faculties to develop and evaluate algorithms for joint GPU cache management and query scheduling in LLM serving systems. The project will begin with a literature review and hands-on exploration of an open-source inference framework such as vLLM. The student will learn how LLM requests are processed, how KV caches are allocated and evicted, and how scheduling policies affect latency and throughput.

The student will then implement a comprehensive simulation or experimental testbed for evaluating different workload-management strategies. Initial tasks may include reproducing baseline policies, reading research papers, collecting performance traces, identifying unique serving characteristics, and analyzing the relationship between cache availability, memory pressure, query length, and response latency.

Based on these observations, the student will help design improved scheduling algorithms. Potential approaches include priority-based scheduling, cache-aware admission control, workload prediction, and online optimization. The student will compare the proposed methods against standard baselines using metrics such as time to first token, inter-token latency, throughput, GPU utilization, memory utilization, and energy consumption.

The student will participate in regular research meetings, document the implementation, summarize experimental results, and prepare a final presentation and technical report. Depending on the results, the student may also contribute to an open-source release and the preparation of a research manuscript.

Skills required:
The student should have a solid background in computer science, computer engineering, electrical engineering, or a related field. Proficiency in Python is required. Experience with machine learning frameworks such as PyTorch is preferred. Familiarity with algorithms, operating systems, optimization, machine learning, or computer architecture will be helpful. Prior experience with GPUs, large language models, or reinforcement learning is beneficial but not required. The student should be comfortable reading technical papers, conducting experiments, and working with open-source software.

Research area: I'm specialized in machine learning.

Student roles:
- Literature Review: Conduct a comprehensive review of existing work on relevant methodologies.
- Model Implementation: Develop and implement models using deep learning frameworks (e.g., PyTorch). This includes adapting existing architectures and designing new components tailored to specific tasks.
- Designing and carrying out experiments on benchmark datasets, evaluating model performance across various scenarios.
- Analyzing experimental results to assess model effectiveness and provide insights into the behavior and limitations of the approaches.
- Writing well-documented code and progress reports, preparing manuscripts or presentations if applicable.

Skills required:
The student should have
1. Strong background in machine learning, particularly in deep learning models.
2. Proficiency in Python and experience with ML frameworks such as JAX or PyTorch.
3. Familiarity with generative models, particularly the fundamentals of diffusion models.
4. Effective communication skills in both writing and speaking in English.

Research area: My research focuses on developing efficient learning methods that reduce the need for supervision. The objective is to leverage the exponentially growing volume of data generated by the internet and connected systems (e.g., social media, the Internet of Things) to build more capable deep learning models while reducing both their computational requirements and their dependence on costly human annotations. My work primarily focuses on visual recognition and understanding tasks involving images and videos.

Student roles:
The student will contribute to the development, implementation, and evaluation of algorithms related to the proposed project. To support this work, the student will conduct a literature review of relevant scientific publications to gain a comprehensive understanding of the state of the art in the field.

The student will learn to use and efficiently share the computational resources available in the laboratory, including GPU servers and software infrastructure, in order to perform large-scale experiments. They will be responsible for designing experiments, analyzing results, and assessing the performance of the proposed methods.

The student will also be expected to document their work and disseminate the results through technical reports and, when appropriate, scientific publications.

The student will be integrated into a dynamic research environment and hosted within my laboratory, where they will interact with MSc and PhD students working on related topics in machine learning and computer vision. The student will receive regular supervision from me and will be paired with a more experienced graduate student who will provide day-to-day guidance and technical support throughout the project.

In addition, the student will have the opportunity to participate in the laboratory's regular activities, including reading groups, research seminars, invited talks, and other scientific and social events, providing valuable exposure to current research trends and fostering professional development.

Skills required:
The ideal candidate will have a strong mathematical background, solid programming skills (Python/PyTorch), and prior experience with Deep Learning and Computer Vision. The student should be comfortable developing and implementing algorithms, analyzing experimental results, and collaborating with other students as part of a multidisciplinary research team.

Research area: My research interests revolve around smart radio environment with more emphasis on (i) interpretable machine learning for communication network, (ii) intelligent reconfigurable surfaces (IRS) aided communication and (iii) embedded lite AI models for low power internet of things. My research projects tackle the open issues on using different learning approaches in combination with different optimization techniques for efficient and interpretable end-to-end communication learning, and shade light on real time beamforming and IRS control with constraints dictated by the channel estimation overhead. Herein, machine learning is also considered as a potential enabler in realising the vision of IRS- empowered smart radio

Student roles:
The student will conduct surveys on RISC-V based embedded system for low power IoT and embedded AI.
The intern will get familiar with RISC-V tools on FPGA Resources and implement RISC-V on FPGA.
With the collaboration with PhD students, the intern will implement RISC-V based embedded AI for low power IoT by proposing energy efficient extended set of instructions and co-processor architectures that impelement mixed-precision matrix-matrix operation engine for flexible and energy-efficient linear algebra and TinyML training acceleration. Exploring approximate computing techiques is another approach that need to be investigated.

Skills required:
1. Embedded systems
2. FPGA design using Vivado
3. VHDL

Research area: My research focuses on developing efficient learning methods that reduce the need for supervision. The objective is to leverage the exponentially growing amount of data generated by the internet and connected systems (e.g., social media, Internet of Things) to build more capable deep learning models while reducing both their computational requirements and dependence on costly human annotations. My work is primarily applied to visual recognition and understanding tasks involving images and videos.

Student roles:
The student will contribute to the development, implementation, and evaluation of algorithms related to the proposed project. To support this work, the student will conduct a literature review of relevant scientific publications to gain a comprehensive understanding of the state of the art in the field.

The student will learn to use and efficiently share the computational resources available in the laboratory, including GPU servers and software infrastructure, in order to perform large-scale experiments. They will be responsible for designing experiments, analyzing results, and assessing the performance of the proposed methods.

The student will also be expected to document their work and disseminate the results through technical reports and, when appropriate, scientific publications.

The student will be integrated into a dynamic research environment and hosted within my laboratory, where they will interact with MSc and PhD students working on related topics in machine learning and computer vision. The student will receive regular supervision from me and will be paired with a more experienced graduate student who will provide day-to-day guidance and technical support throughout the project.

In addition, the student will have the opportunity to participate in the laboratory's regular activities, including reading groups, research seminars, invited talks, and other scientific and social events, providing valuable exposure to current research trends and fostering professional development.

Skills required:
The ideal candidate will have a strong mathematical background, solid programming skills (Python/PyTorch), and prior experience with Deep Learning and Computer Vision. The student should be comfortable developing and implementing algorithms, analyzing experimental results, and collaborating with other students as part of a multidisciplinary research team.

Research area: My specialized research area is Natural Language Processing (NLP). NLP is one of the most important and useful fields of artificial intelligence, dealing with automatic text processing. Its widespread application is due to the ubiquity of language and text as a main human communication channel, e.g., emails, blogs, social media, web search, chatbots, medical reports, etc. The objective of my research is to devise new methods for machine language understanding, which include language representation, summarization, and classification from unstructured and unconventional text streams mainly in an unsupervised fashion.

Student roles:
Collect and prepare relevant conversational data, including crawling online sources and curating datasets rich in emotional content, with particular attention to rare or underrepresented emotions (e.g., fear, disgust, surprise).

Assist in the creation and/or annotation of an emotion recognition corpus, including cleaning, preprocessing, and contributing to the development of annotation guidelines.

Conduct a literature review on emotion recognition, empathy modeling, and LLM-based conversational agents by synthesizing research from leading NLP and AI venues; critically assess current limitations and opportunities, especially regarding rare emotions.

Design and implement emotion recognition methods using large language models (LLMs), focusing on:

inferring the most contextually appropriate emotion to express for an empathetic reply, and

anticipating the user's emotional reaction to a generated utterance.

Experiment with both prompt-based and fine-tuning approaches for adapting LLMs to emotion-rich conversational tasks, and evaluate performance across common and rare emotion categories.

Regularly communicate progress through presentations and reports, and engage in collaborative discussions with graduate students and research mentors.

Gain exposure to research best practices, experiment reproducibility, and ethical considerations in the development of emotionally responsive AI agents.

Skills required:
Expertise in any of these fields: Computer Science, Computer Engineering, Information Science and Technologies. Prior programming experience in Python. Experience with Large Language Models prompting and Fine-tunning. Experience with PyTorch. Knowledge of basic multivariate calculus, linear algebra, probability, and statistics. Familiarity with the essentials of data mining, machine learning, or artificial intelligence. Being previously introduced or worked on a project involving NLP.

Research area: My general research area is Software Engineering. In particular, my main research focuses on improving (AI) software performance and developing fast and efficient software in fast-paced development and release practices (e.g., DevOps). My detailed research interests include software performance engineering, AI software system quality, and web GUI testing.

Student roles:
Literature Review: Read and summarize 3-5 core papers on LLM reliability and differential testing.
Dataset Preparation: Clean and format established coding benchmarks such as HumanEval or MBPP.
Scripting & Mutation: Write Python scripts to generate semantically equivalent prompt variations (mutants) to test model robustness.
Data Collection: Automate API calls to various LLM versions and capture code execution results.
Analysis & Writing: Visualize regression patterns and assist in drafting a 4-10 page paper.

Skills required:
Strong proficiency in Python programming.
Familiarity with GitHub and basic Linux command-line operations.
Basic understanding of Software Testing principles (e.g., unit testing, regression testing).
Interest in Natural Language Processing (NLP) and Large Language Models.

Research area: The proliferation of the Internet of Things (IoT) has led to an increasing demand for wireless access solutions capable of connecting low-power devices over large areas with low data rates. My research addresses this demand by focusing on enabling massive wireless connectivity for IoT. This involves strategies such as augmenting capacity through advanced wireless access techniques, enhancing reliability in challenging radio conditions, and exploring the possibilities of integrating satellite connectivity to extend the reach of IoT connectivity.

Student roles:
As a student in our lab, you will play a crucial role in advancing our research on wireless connectivity for the Internet of Things (IoT). Your responsibilities will include:

- Configuring and Programming IoT Devices: Set up and program various IoT devices equipped with sensors.
- Designing Measurement Protocols: Develop protocols to measure the performance and connectivity of IoT devices.
- Conducting Measurements: Execute measurement campaigns in diverse environments to gather data on device performance.
- Analyzing Data: Interpret the results from measurements to inform the development of new technologies.
- Collaborating on Research: Work closely with other team members to contribute to the overall research objectives and publications.
- Participating in Field Tests: Engage in field tests and real-world scenarios to validate our technologies, such as taking devices on bus rides, bike trips, and city strolls.

This role offers hands-on experience with cutting-edge technologies and real-world applications, providing a comprehensive understanding of advanced wireless communication systems for IoT.

Skills required:
We are seeking motivated students with a background in wireless communications, signal processing, or related fields. Candidates should have experience with programming (e.g., Python, C/C++), basic understanding of IoT technologies, and familiarity with data analysis. Hands-on experience with hardware and sensors is a plus. Ideal candidates are problem-solvers with a keen interest in cutting-edge research, capable of working both independently and as part of a team.

Research area: My research focuses on advancing the development of high-performance, highly scalable, and highly available distributed systems, which serve as the foundation for modern computing infrastructures. In particular, I study how to design efficient algorithms, protocols, and system architectures that power distributed AI systems and database management systems (DBMS). Our work bridges AI, databases, and cloud systems, addressing challenges in scalability, efficiency, and real-world deployment, with the goal of enabling next-generation intelligent data systems.

Student roles:
The student will contribute to the design, implementation, and evaluation of a system that integrates machine learning with blockchain technologies. The role involves building core components for model training, fairness monitoring, and blockchain-based logging or coordination.

The student will be responsible for coding and system development, including implementing machine learning workflows and integrating them with a blockchain layer. They will also conduct experimental evaluations, measuring both model fairness and system performance.

The student will work closely with graduate students, participating in system design discussions and iterative development. This role emphasizes hands-on implementation, experimentation, and understanding how to build trustworthy and transparent AI systems.

Skills required:
Students should have a background in computer science or computer engineering, with a solid understanding of data structures and algorithms. Strong programming skills in Python are required. Familiarity with Golang is a plus but not required. Interest in machine learning, distributed systems, or blockchain technologies is highly beneficial.

Research area: My research interests include navigation and mapping for autonomous systems, with a focus on end-to-end driving models, GNSS/INS integration, and visual-inertial SLAM. I am particularly interested in leveraging machine learning to improve localization robustness, quantify uncertainty, and enhance real-time decision-making in complex environments.

Student roles:
Student Role – Autonomous Driving System Development
• Simulate and calibrate GNSS, IMU, camera, and LiDAR sensors in CARLA
• Design road networks, traffic scenarios, and urban infrastructure features
• Implement localization methods (e.g., dead reckoning, Kalman filtering) and integrate with map data
• Develop perception modules for lane and object detection
• Train end-to-end driving models using multi-sensor input
• Integrate perception, localization, and control modules into a unified CARLA-based system

Skills required:
The student should have a foundational background in robotics, computer vision, or machine learning, with some experience in Python programming. Familiarity with deep learning frameworks such as PyTorch or TensorFlow is beneficial. Interest in autonomous systems and simulation platforms like CARLA is expected. Exposure to sensor data (e.g., camera, LiDAR, GNSS, or IMU) and tools such as ROS is an asset. The role requires the ability to work with code and solve technical problems as part of a larger system development effort.

Research area: My research develops algorithms for decision-making under uncertainty, with applications to networked systems, such as smart grids, cloud computing and LLM inference systems. The central theme of my work is to understand how algorithm performance scales with the structural properties of the underlying systems (e.g., objectives, constraints, online dynamics, and multi-agent interactions), and the nature of environmental uncertainties (e.g., adversarial, stochastic, or predictive uncertainty). My goal is to design algorithms with provable guarantees for efficiency, robustness and fairness, enabling reliable resource allocation and scheduling in real-world networked systems.

Student roles:
The main responsibility of the student is to develop and evaluate optimization-based scheduling methods for energy storage systems that support large energy consumers under limited grid capacity.

The student will first review relevant literature on energy storage scheduling, peak-load reduction and demand charge management. Based on this review, the student will help formulate the offline optimization problem that captures demand profiles, electricity prices, storage capacity, charging and discharging limits, efficiency losses, and grid capacity constraints. This model will serve as a benchmark for understanding the value of storage and the trade-offs among grid capacity, storage size, and electricity cost. Building on the offline model, the student will implement real-time scheduling methods for uncertain and time-varying demand and prices. This may include model predictive control and other online optimization approaches that update storage decisions as new information becomes available. The student will also design simulation experiments to compare the proposed methods with baseline policies, such as no-storage operation, rule-based charging and discharging, or price-driven heuristics.

On the empirical side, the student will collect, process, and analyze real-world data traces, such as electricity demand profiles and time-varying electricity prices. The student will implement the optimization models and algorithms, run numerical experiments, and evaluate system performance in terms of peak power reduction, electricity cost savings and feasibility under grid capacity limits.

The student will also participate in regular research meetings, present progress, discuss technical challenges, and help prepare research outputs, including project reports, presentation materials, and potential conference or journal submissions. Through this work, the student will gain hands-on experience in optimization, energy systems, and data-driven scheduling for emerging large energy consumers.

Skills required:
The student is expected to have a solid mathematical background, including linear algebra, calculus, and probability. The student should also have basic knowledge of control and optimization. Prior experience in power systems or smart grids is preferred but not required.

The student should have good programming skills and be proficient in at least one programming language, such as Python. Experience with simulation or mathematical programming tools is preferred.

Research area: Software Programming, Cloud Computing, Embedded and real-time Systems, Internet of Things, Machine Learning

Student roles:
• Relevant Literature review
• Contribution to the optimisation problem formulation
• Investigation of the potential solution algorithm design and analysis
• Contribution to the implementation and experimentation and discussion of the results.
• Writing a project report

Skills required:
The candidate should have good programming skills, preferably in Python or a similar language. Basic knowledge of cloud computing, containers, Kubernetes, or distributed systems would be an asset. Familiarity with artificial intelligence, machine learning, or reinforcement learning would also be useful. The student should be interested in system experimentation, performance monitoring, resource management, and energy-efficient computing. The ability to read scientific papers, analyze experimental data, and prepare technical reports is desirable.

Research area: Software Programming, Cloud Computing, Embedded and real-time Systems, Internet of Things, Machine Learning

Student roles:
• Relevant Literature review
• Contribution to the optimisation problem formulation
• Investigation of the potential solution algorithm design and analysis
• Contribution to the implementation and experimentation and discussion of the results.
• Writing a project report

Skills required:
The candidate should have good programming skills, preferably in Python or a similar language. Basic knowledge of cloud computing, containers, Kubernetes, or microservices would be an asset. Familiarity with artificial intelligence, machine learning, or reinforcement learning would also be useful. The student should be interested in system experimentation, performance analysis, resource optimization, and energy efficiency. The ability to read scientific papers, analyze experimental results, and write technical reports is also desirable.

Research area: I have published over 110 research articles since 2017 and hold 14 US patents in AI, Assistive Technologies, Medical Imaging, Computer Vision, IoT, Robotics, and Deep Learning. I supervised over 30 undergraduate projects, which resulted in 30+ co-authored publications with those students. I received multiple international awards, including a Gold Medal at the 49th Innovation Exhibition in Geneva and first place at the NEOM Oxagon Hackathon in KSA for assistive tech related to the proposed project. I was named among the Top 2% Scientists by Stanford University in 2024, 2025 and also became an IEEE Senior Member in 2025.

Student roles:
Under my supervision, the student will contribute to the research, design, development, and evaluation of an AI-driven breast cancer detection system using histopathological images. The student will assist in collecting, preprocessing, and organizing medical imaging datasets, including histopathology slides, ensuring proper annotation and patient-wise data splitting for robust model evaluation.
The student will support the development and training of deep learning models, including CNNs and Vision Transformers, for breast cancer classification and feature extraction. This includes data preprocessing, augmentation, feature engineering, model training, validation, and performance evaluation. The student will also contribute to the design and implementation of ensemble learning strategies, hybrid feature fusion, and automated model selection mechanisms to improve classification accuracy and robustness.
In addition, the student will assist in integrating attention mechanisms to highlight diagnostically relevant regions within histopathological images and applying dimensionality reduction techniques such as PCA, autoencoders, and bottleneck architectures to optimize feature representations. The role also includes supporting the development of explainable AI (XAI) methods to improve interpretability and clinical trust in the proposed system.
The student will participate in conducting experiments, benchmarking results against existing methods, and validating model generalization on external datasets. Responsibilities may also include documentation of research findings, code optimization, and assisting in preparing visualizations and reports for publication.
This position provides hands-on experience in deep learning, medical image analysis, ensemble AI systems, and explainable artificial intelligence. Through this work, the student will gain advanced research and development skills while contributing to an impactful healthcare AI solution aimed at improving breast cancer diagnosis accuracy and reliability.

Skills required:
I am seeking a motivated intern student with a background in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or related fields. Ideal candidates should have experience in machine learning, deep learning, and medical image analysis. Proficiency in Python and familiarity with frameworks such as TensorFlow, PyTorch, or Scikit-learn are required. Knowledge of convolutional neural networks, vision transformers, and image processing is highly desirable. Experience with histopathological or medical imaging datasets is an asset. Strong analytical thinking, problem-solving, communication, and teamwork skills are essential, along with an interest in AI for healthcare applications and medical diagnosis.

Research area: My areas of research are computer vision and machine learning for processing complex and unstructured data. In particular, the use of new approaches such as Deep Learning and multimodal imaging in the fields of security and biometrics, UAVs, medical imaging and intelligent robotics.

Student roles:
Reading of some documents related to the subject.
Developing vision and machine learning algorithms using existing libraries.
Writing documentation/report on the work done.

Skills required:
Programming (Python, Matlab, etc.)
Machine leaning or image processing are considered a plus

Research area: My expertise is AI and Machine Learning. My current research focuses on multi-omics and sequence data analysis which provides novel information on the mechanisms of the biological process. We develop computational tools for complex and high-dimensional data. I'm particularly interested in applying deep learning and AI to complex data and solve real-life problems.

Student roles:
- Collect data and complete necessary preprocessing
- Survey existing literature on the practice of using GenAI on business report analysis.
- Apply popular GenAI models and compare the key features extracted from the report.
- Develop detailed documentation and the user manual of the developed models and publish it to the online repository, e.g. GitHub
- Write a final report summarizing the project

Skills required:
The student is expected to have solid programming skills using Python. Experience of High performance computing and Linux system are essential. Knowledge of economics is preferred but not required. Good communication skills and writing skills. Knowledge of genomics is an asset but not required for this project.

Research area: My long-term research objective focuses on intra-operative target tracking for radiotherapy. In fact, real-time assessment and feedback enable clinicians to make necessary adjustments and improve the outcome of the procedure. I have a decade of experience in biomedical computing and medical devices product research, development and management. Before becoming an assistant professor at Bishop’s University and then at Toronto Metropolitan University, I used to work for a leading company in the radiotherapy industry. My research goals focused on organ tracking using medical images.

Student roles:
Develop and implement methods.
Apply the methods on datasets.
Analyze and document results.
Write conference/ journal paper.

Skills required:
Knowledge of Python.
Strong logic and fundamentals in statistics

Research area: The Autonomous Robotics and Convex Optimization (ARCO) Lab applies cutting-edge optimization techniques to fundamental problems in robotics and related fields. ARCO Lab is presently focused on designing efficient algorithms for working with a variety of spatiotemporal data at scale. These methods enable robust perception and planning with formal safety guarantees, providing a complementary approach to the widespread use of black-box deep learning-based models. ARCO Lab’s vision for deploying safe and interpretable autonomy is to develop tools for non-roboticist end-users working in space exploration, manufacturing, medical robotics, and the geosciences.

Student roles:
The intern will be welcomed into the ARCO Lab team and given a desk in a shared office space. The student will have the opportunity to attend seminars and other events occurring at McMaster and network with world-class researchers in a variety of fields. They will work closely with graduate students and undergraduate research assistants on a daily basis and meet with Dr. Giamou on a weekly basis. Initial meetings will focus on getting the intern up to speed on relevant technical material and planning various milestones for the project. As the internship progresses, the intern is expected to exhibit increasing autonomy and ownership of their project.

Over the course of the internship, a successful intern will work with geoscientists to define a useful environmental monitoring mission, mathematically formulate an active perception-based approach to this mission, and implement experiments in simulation and real hardware. The intern will be given the opportunity to present their findings to the lab and its collaborators, and will also contribute to the preparation of a research paper for a top-rated international journal or conference on robotics and automation. While the goals and methodology of the project are well defined, there is considerable flexibility in the specific approach the student can take to tackle the problem, and the expectation is that the intern will develop independent research skills under the close mentorship of Dr. Giamou. The intern will also be encouraged to explore unexpected results and promising avenues for research, and to assist their colleagues with related projects when appropriate.

Skills required:
In addition to enthusiasm, scientific curiosity, and effective communication skills, this project requires significant engineering experience. Qualified candidates will have strong programming skills, experience working with sensors and other hardware, familiarity with state estimation and computer vision, and experience using software for working with real and simulated mobile robot platforms (e.g., ROS/ROS2, Gazebo, PyBullet). Exceptional candidates will also have experience with one or more of the following topics: simultaneous localization and mapping (SLAM), programming in C/C++, designing and deploying micro aerial vehicles, the theory of active perception, and advanced probability and statistics.

Research area: Professor Erechtchoukova's research is in the fields of data-driven and model-driven decision support in environmental sustainability, application of Natural Language Processing techniques to document categorization and developing question answering systems in low resource domains, application of machine learning techniques to water resource management, simulation modeling of complex systems, data modeling for semistructured data, and optimization of scheduling systems. Current research projects are devoted to developing hybrid predictive modeling frameworks for integrated hydrology, application of artificial intelligence for education, optimization of healthcare scheduling systems, sustainability appraisal, and optimization of environmental monitoring.

Student roles:
An intern will become a team member working on application of machine learning tools for environmental assessment and forecasting. The student will use a computer and provided software to work with environmental databases to extract the data. They will implement basic data analysis and data transformation corresponding to required computational experiments. The intern may develop a computer code for that. They will test various data mining algorithms on extracted environmental data and compare the results to identify those with extended lead times of predictions and appropriate ways to aggregate the results. The conducted analysis will help to modify the framework and refine major steps in its workflow.

Skills required:
Interest in Big data analytics, Artificial Intelligence, and environmental management problems is a must; desire to learn and explore new computational tools is a must; ability to understand software manuals and use the software to run computational experiments; ability to use electronic spreadsheet software (e.g. MS Excel); willingness to work with databases and large data sets; ability to write simple computer code in Python or R, understanding of basic statistics; ability to implement repetitive tasks. Familiarity with data mining, machine learning approaches, and environmental data analysis is an asset.

Research area: My research focuses on operations research and supply chain management, with particular expertise in stochastic optimization, vehicle routing, and humanitarian logistics. I design mathematical programming models and solution methods for problems where decisions must account for uncertainty and heterogeneous populations, such as donation coordination, disaster response, and resource allocation under capacity constraints. My work bridges quantitative modeling with applied, real-world decision contexts, often involving vulnerable populations or critical time windows. I am especially interested in models that explicitly incorporate equity and risk considerations alongside efficiency.

Student roles:
The student will be expected to: conduct a literature review on evacuation routing, humanitarian logistics, and equity-aware optimization; characterize and formalize the relevant population segments (car ownership, age group, cardiac/respiratory health status) and translate them into model parameters and constraints; develop a mathematical formulation for the evacuation routing/allocation problem, considering both exact (MIP) and heuristic/matheuristic solution approaches; build or adapt realistic test instances for a Québec region; conduct computational experiments and sensitivity analyses comparing efficiency- and equity-based objectives; document methodology and results with the goal of producing a manuscript for submission to a peer-reviewed journal; and present progress regularly in research group meetings.

Skills required:
Strong background in operations research, applied mathematics, or industrial engineering, with solid programming skills (Python or Julia) and familiarity with optimization solvers (Gurobi, CPLEX, or open-source equivalents). Prior exposure to vehicle routing, stochastic/robust optimization, or humanitarian logistics is a strong asset. Sensitivity to equity considerations in resource allocation and the ability to translate qualitative constraints (e.g., health status, vulnerability) into quantitative model parameters is important. Strong written communication skills in English or French are required for eventual publication.

Research area: My research is in the area of programming languages, with a primary focus on program verification and program synthesis. Specifically, program verification aims to determine whether a given program always satisfies its specification. Program synthesis seeks to automatically generate a program that satisfies a given specification, which may be expressed through input-output examples, logical formulas, natural language descriptions, reference implementations, or their combinations.

Student roles:
Students joining this project will obtain hands-on experience with equivalence checking of database queries and learn cutting-edge program verification techniques.

Students are expected to perform the following tasks:
1. Read related papers and understand the strengths and weaknesses of existing equivalence checking techniques;
2. Learn how to use a modern constraint solver;
3. Develop an equivalence checking technique for database queries;
4. Evaluate the effectiveness and refine the technique if necessary;
5. Write a technical report.

If multiple students join this project, students will coordinate with each other and finish the project together.

Skills required:
The following background and skills are required for the students:
1. Major in computer science or related fields;
2. Familiar with principles of compilers and/or foundations of programming languages;
3. Knowledgeable of formal verification;
4. Proficient in the Java, C++, or Python programming language;
5. Good communication skills;
6. Enthusiastic about research.

The following background and skills are not required but would be a plus:
1. Familiar with constraint solving and automated logical reasoning;
2. Has previous experience in formal verification.

Research area: Our lab researches high-fidelity distributed interaction, with both real and virtual data, to support the most demanding applications, regardless of sensor and bandwidth limitations. We work with audio, video, and haptic technologies, mixed reality and mobile computing, building systems that leverage their capabilities to facilitate and enrich both human-computer and computer-mediated human-human interaction. Active projects include development of conversational avatars for therapy and engagement with seniors, rendering audio-haptic experiences of graphics contents for users who are blind, multimodal immersive walking experiences, telepresence for music and social interaction, and design of the flight deck of the future.

Student roles:
The student will be involved in one or more of the following activities, based on a combination of identified priorities, skill set, and status of the project at the time of the internship:

1. Developing prototype triage agent to obtain health and symptom indicators from patient, and processing this information against the Canadian Triage and Acuity Scale (CTAS) to determine intake priority.
2. Developing a prototype "standard patient module" employing conversation and possibly avatar animation for training of clinicians to interact with patients and carry out diagnostic routines.
3. Developing a prototype scheduler module for booking appointments based on clinician availability and integrating the system with a telephony interface for testing.

Details as to these tasks will be refined according to the present state of system development nearer the time of the internship start date, and the priorities expressed by our partners in the health care sector.

Skills required:
The student should have strong programming skills, and in particular, experience building systems supported by generative AI backends. Experience with graphical avatar rendering, speech recognition, speech synthesis, and computer vision, would all be considered beneficial but not imperative.

Research area: This project sits at the intersection of applied AI and digital heritage. The research area is agentic retrieval-augmented generation (RAG): building AI systems that plan their own reasoning steps, query structured databases, verify outputs, and produce cited responses. Unlike systems that rely on large pre-trained models alone, agentic RAG grounds answers in verified local data. The application domain is Canadian heritage, specifically the Historic Sites of Manitoba database, accessed through a locally governed, privacy-preserving server hosted at Canadian Mennonite University.

Student roles:
This role is what AI researcher Andrej Karpathy calls "agentic engineering": not training models, but orchestrating them — designing the pipelines, tools, and verification loops that make LLM-based systems reliable and grounded. The intern will also be expected to work fluently with AI coding agents as development tools, using them to accelerate implementation, scaffold components, and navigate an unfamiliar codebase. Comfort leveraging these tools effectively, while exercising the judgment to verify and own the output, is itself a valued skill on this project.
The intern will join a live research project at a critical development juncture. A team of computational mathematics undergraduates will have been building toward a working agentic RAG system for two semesters. The intern's first task will be to assess the current state of the codebase, identify the highest-priority development gaps, and work with the faculty supervisor to scope the 12-week contribution accordingly.
From there, the intern will take a senior technical role, bringing software architecture experience and application development discipline that complements the team's Python and data analysis strengths. Depending on where the project stands, this could mean refactoring existing components into a clean modular application, implementing the next layer of the agentic pipeline, integrating the system with the MHS InSite mobile platform via API, or running the first structured evaluation cycles. Most likely it will involve some combination of these.
Throughout, the intern will document their work for the project's public GitHub repository. Weekly check-ins with the faculty supervisor are expected. Strong communication skills and comfort contributing to a small, collaborative, fast-moving research team are important.

Skills required:
We are looking for a computer science student with hands-on experience building software applications, not just scripts or notebooks. Required: proficiency in Python, familiarity with REST APIs and modular software design, and exposure to at least one relevant area — NLP, information retrieval, LLM integration, or database-backed applications. Experience with version control (Git) is expected. Prior work with agentic AI frameworks (LangChain, LlamaIndex, or similar) and comfort using AI coding agents (GitHub Copilot, Cursor, Claude, or similar) as development tools are both strong assets but not required.

Research area: My research spans data privacy, cloud computing, cybersecurity, social network analysis, and AI/machine learning. I have addressed challenges in safeguarding personal data in cloud environments and developed secure, reliable systems. I proposed a cloud solution incorporating an entropy-based trust computation approach for data-as-a-service environments. Additionally, I have worked on heterogeneous information networks, focusing on healthcare data privacy, where I developed a differentially private algorithm for releasing de-identified health data with reduced information loss and enhanced scalability. My work also includes contributions to user credibility systems in social networks, blockchain-based solutions, as well as supply chain cybersecurity risk analysis.

Student roles:
The student will contribute to a multidisciplinary research project that examines the ethical, legal, and societal impacts of artificial intelligence (AI) technologies used in public safety, such as surveillance and predictive systems. The goal is to support the development of responsible governance practices that protect civil liberties while addressing security concerns.

The student will assist in conducting a literature review focused on AI surveillance, privacy laws, and ethical frameworks. They will support the design and implementation of qualitative research methods, including surveys and interviews with key stakeholders such as members of the public, policymakers, and security professionals.

They will help collect and analyze qualitative data to understand public perceptions, ethical concerns, and regulatory gaps related to AI in public safety. The student will also contribute to the development of an ethical framework and policy recommendations that reflect both public expectations and best practices for AI governance.

In addition, the student will assist in drafting academic papers, conference submissions, and policy briefs. Strong communication and academic writing skills are essential for translating complex findings into accessible formats for academic, policymaking, and public audiences.

The student may also support the planning and delivery of knowledge mobilization activities such as stakeholder workshops and public presentations. This experience will help the student build skills in interdisciplinary collaboration, ethical analysis, and research communication.

Overall, the role will provide the student with valuable exposure to applied research at the intersection of technology, ethics, and policy. They will gain hands-on experience in qualitative methods, legal and policy analysis, and public engagement, while contributing to a socially relevant project with real-world impact.

Skills required:
- Background in computer science, public policy, sociology, law, or a related discipline
- Strong interest in the social and ethical impacts of technology
- Experience with qualitative research methods (e.g., interviews, surveys)
- Familiarity with AI ethics, privacy laws, or surveillance technologies is an asset
- Excellent communication and academic writing skills for research papers and conference/journal submissions
- Ability to engage effectively with policymakers, stakeholders, and community groups
- Comfortable working in an interdisciplinary research environment
- Committed to exploring issues at the intersection of technology, civil liberties, and public safety

Research area: My research spans data privacy, cloud computing, cybersecurity, social network analysis, and AI/machine learning. I have addressed challenges in safeguarding personal data in cloud environments and developed secure, reliable systems. I proposed a cloud solution incorporating an entropy-based trust computation approach for data-as-a-service environments. Additionally, I have worked on heterogeneous information networks, focusing on healthcare data privacy, where I developed a differentially private algorithm for releasing de-identified health data with reduced information loss and enhanced scalability. My work also includes contributions to user credibility systems in social networks, blockchain-based solutions, as well as supply chain cybersecurity risk analysis.

Student roles:
The student will contribute to a research project focused on evaluating the practical deployment characteristics of post-quantum cryptographic systems. The project will begin with a review of post-quantum cryptography concepts, current standards, and emerging implementation challenges associated with quantum-resistant security technologies.
The student will investigate selected post-quantum cryptographic algorithms and become familiar with available software libraries, development tools, and testing environments. Working under supervision, the student will design experiments to measure and compare various performance indicators, including execution time, memory usage, communication overhead, and computational efficiency.
The student will assist in implementing experimental test environments, collecting performance data, and analyzing results across different configurations. The work may involve benchmarking cryptographic operations, comparing alternative implementation approaches, and evaluating the impact of optimization techniques on system performance.
In addition to technical experimentation, the student will contribute to reviewing academic literature, documenting methodologies, interpreting findings, and preparing technical reports summarizing project outcomes. The student will participate in regular research discussions, present progress updates, and collaborate with the research team to refine experimental procedures and research objectives.
The project provides hands-on experience in applied cybersecurity research, performance evaluation, secure system design, and post-quantum technologies while strengthening skills in experimental analysis and technical communication.

Skills required:
• Undergraduate student in Computer Science, Software Engineering, Computer Engineering, Cybersecurity, or a related discipline
• Strong programming skills in Python and/or C/C++
• Basic understanding of computer networks, operating systems, and cybersecurity concepts
• Familiarity with data collection, analysis, and performance evaluation techniques
• Interest in cryptography, secure systems, or emerging technologies
• Ability to read technical documentation and research papers
• Strong analytical, problem-solving, and communication skills

Research area: This research area combines applied software development, educational technology, and the evaluation of learning interventions in technical and safety-critical domains. This project is part of an interdisciplinary aviation education initiative focused on simulation-based training for Air Traffic Control communication. The work involves mobile learning, speech-based interaction, structured scenario design, performance-based assessment, learning analytics, and data visualization. More broadly, I am interested in how computing tools can support measurable skill development in contexts where accuracy, timing, protocol adherence, and decision-making are important. The project connects computer science with aviation training and Scholarship of Teaching and Learning research.

Student roles:
The Globalink student will contribute to the research and development infrastructure for the ATC communication training project. The exact tasks will depend on the state of the app and data collection by summer 2027, but the student’s role will focus on helping the team prepare the software and data pipeline for broader evaluation.

One possible responsibility is data processing and visualization. The app will collect anonymized student performance data such as scenario identifiers, turn-by-turn responses, speech-to-text transcripts, scoring results, response times, error categories, and completion history. The student may help clean and organize these data, design export formats, prepare reproducible analysis scripts, and create dashboards that show student progress, common error types, response-time trends, and scenario-level performance.

A second possible responsibility is app-readiness and software support. The student may help improve the Flutter app, test iOS/Android workflows, refine and improve multi-turn scenario support, verify that settings such as turn-by-turn feedback work correctly, and contribute to documentation for future developers. They may also help build small tools that allow aviation collaborators to describe scenarios in a structured format that can later be imported into the app.

A third possible responsibility is research support. The student may help prepare anonymized datasets for analysis, document data fields, support quality checks, and create visual summaries for the project team. They may also assist with preparing figures or preliminary results for a teaching-and-learning publication, poster, or grant report.

The student will work closely with the supervisor and may interact with aviation, education, and statistics collaborators. They will use GitHub for version control and issue tracking, and will be expected to make clear, documented, and testable contributions. The project is appropriate for a student who wants experience with applied software development, learning analytics, educational technology, and interdisciplinary research.

Skills required:
The student should have a background in computer science, software engineering, data science, or a related field. Experience with Python, Dart/Flutter, or another programming language is required. The student does not need prior aviation experience, but should be willing to learn aviation communication concepts and work carefully with domain-specific requirements. Helpful skills include data cleaning, visualization, dashboard development, mobile or web development, Git/GitHub, and basic software testing. The ideal student is organized, communicates clearly, documents their work, and is comfortable working on an applied interdisciplinary research project.

Research area: This research will be at the intersection of computer science, data science, AI NLP, and global digital health research. Our work spans across North American and African countries with a focus on low- and middle-income settings. We develop and deploy digital health tools that improve healthcare quality and access by analyzing real-world clinical and public health conversations between patients and providers in outpatient and remote care settings.

Student roles:
The student will join an interdisciplinary team actively building ConVIScope — an AI-powered dashboard for analyzing real-world healthcare conversations. The student's role will include:
a) NLP & Data Science: Develop, optimize, and fine-tune NLP models on our clinical conversation dataset. Tasks include building models for text classification, sentiment analysis, and topic modeling; extending model capabilities across languages (English, French, Kinyarwanda) and clinical contexts (HIV, diabetes, TB, cardiology); and developing new prediction and recommendation functions that surface actionable clinical insights for healthcare providers.
b) Software & Visualization: Contribute to the design and development of the ConVIScope dashboard, translating NLP outputs into intuitive, clinically meaningful visualizations. Tasks include building UI/UX features for healthcare provider users, developing data pipeline integrations, and conducting iterative design with clinical and public health stakeholders.
Both tracks involve direct collaboration with clinicians and researchers across Canadian and East African partner sites, with opportunities to co-author publications and present findings. The student will gain hands-on experience with real patient data (under appropriate ethics approvals), building skills in responsible AI development for global health.

Skills required:
Strong foundation in machine learning and NLP, including model development, optimization, and fine-tuning. Experience with Python and relevant libraries (e.g., HuggingFace, spaCy, scikit-learn). Familiarity with multilingual or low-resource NLP is an asset. Students with a background in software engineering, data visualization, or UI/UX design are also encouraged to apply, particularly those with experience building interactive dashboards or data pipelines. Interest in global health or human-centered design is strongly valued.

Research area: My research work focuses on sensing and decoding human brain states using electroencephalography or commonly known as EEG signals - a popular modality to build Brain-Machine Interfaces (BMI) or Brain-Computer Interfaces (BCI). Using a person’s brain state as their unique signature, I have built BMI applications such as biometric keys, sensing driver distraction, and developing an index of blood flow regulation. However, BMI has also opened up a new frontier of cybersecurity and data analytics challenges. Thus, my academic interests are focused on building secure and resilient context-aware BMI applications to elevate day-to-day human experiences.

Student roles:
The intern would work with Dr. Bajwa to extend her existing research on the security and privacy of Brain-Machine Interfaces. The main tasks will include:
- Create a local database of publicly available informed consents for EEG data sets.
- Create appropriate tags for the type of data collected.
- Extract information about the sensitivity of information from the description of the EEG tasks in the informed consent.
- Create two versions of informed consent (the EEG data collection will not take place). One of the consents will include information about the potential security threats associated with EEG data and the other consent will follow a general template.
- Administer two different questionnaires based on the two informed consents.
- Develop metrics to compare and quantify the differences in the responses of the two informed consents.

The intern will develop the ability to carry out data mining research and produce publishable work in peer-reviewed forums. A final report and presentation will be required from each intern towards the end of the internship period.

Skills required:
The student should have the following desired skill sets:
- Be proficient in coding one or more programming languages such as Python, Java, or C/C++.
- Have a general familiarity with cybersecurity, data mining, and machine learning concepts.
- Previous experience in working with EEG data or digital signal processing is a plus.
- Have the ability to work as part of a multidisciplinary team.
- Be curious, creative, collaborative problem solver, and results oriented.

Research area: Dr. Travis Esau specializes in precision and digital agriculture, focusing on the design and development of mechanized and autonomous systems to enhance farm productivity, sustainability, and profitability. His research integrates machine vision, artificial intelligence, and geospatial technologies to enable data-driven decision-making in crop production systems such as wild blueberry and potato. He advances innovations in automated harvesting, precision spraying, and field monitoring, while also addressing farm traceability and cost-of-production analysis. Through applied, field-validated research, his work supports the transition toward Agriculture 4.0, improving efficiency, reducing environmental impact, and strengthening the resilience and competitiveness of modern agricultural systems.

Student roles:
The intern will work to develop machine learning models to segment the areas of a wild blueberry field containing the target weeds and to evaluate the deposition of sprayer drones in wild blueberry fields. The primary deliverables will include trained machined models that produce high accuracy prescription maps and complete analysis of the data collected during sprayer testing. Other deliverables will include detailed documentation of the method used to process the drone images and create prescription maps. A detailed testing plan and the subsequent outcomes from the field trials will also be required. Throughout the internship, the intern will submit weekly progress reports to the supervisor with a detailed description of the work they have completed to date and their next week plan.

Skills required:
The student should have a background in precision agriculture, remote sensing, or agricultural engineering, with experience in drone-based data collection and analysis. Familiarity with optical and multispectral imagery, GIS software such as ArcGIS Pro and Agisoft Metashape Professional, and image processing workflows is essential. The student should be comfortable training and evaluating machine learning models for weed detection and generating prescription maps. Experience with experimental design, field data collection, and laboratory analysis is an asset. Strong analytical, organizational, and problem-solving skills are required, along with the ability to adapt to evolving project timelines and collaborate within a research team.

Research area: My research area is bioinformatics which is the computational analysis of biological data. Specifically my research focuses on developing machine-learning based tools for bacterial genomics, transcriptomics and metagenomics. A common research project workflow in my lab is to collect publicly available large-scale data for a specific biological process, extract features amenable for training machine learning models, generate and evaluate machine learning models in terms of their predictive performance and use the best generated model to create a tool for the microbiology research community.

Student roles:
The student will perform the following tasks:
1. Read scientific papers related to chemical compound bioactivity prediction using machine learning and feature extraction from SMILES descriptions.
2. Write Python scripts for feature extraction from SMILES descriptions using available libraries.
3. Implement a pipeline that will take the results of a specific bioactivity screening containing SMILES descriptions and a label indicating whether the given compound has this bioactivity, extract various feature sets from the SMILES descriptions, train and evaluate random forests models with these feature sets using cross-validation and scaffolding, and generate reports summarizing predictive performance per model and in aggregate.
4. Use the models generated in task 3 achieving a predictive performance above certain threshold to predict the bioactivities of all the natural compounds in the COCONUT database.
5. Perform a literature-based evaluation of the most confident predictions from task 4.
6. Place all the scripts used in a well-documented GitHub repository to support reproducible research
7. Write a report of the methods and results.

The student will have weekly meetings with their supervisor to discuss progress and plan the week actions. Student will also attend monthly lab meetings and will present in such a meeting at the end of their internship. Student will learn how to use a high performance computing environment to run the pipeline implemented in task 3 above.

Skills required:
The ideal candidate has a bachelor's degree in bioinformatics, computational biology or computer science; is proficient in Python programming and knowledgeable with Python libraries such as numpy, scikit-learn and scikit-bio; is familiar with supervised machine learning; has excellent communication and troubleshooting skills; and is enthusiastic about this project. Knowledge in R and nextflow programming is an asset. Basic chemistry knowledge will be useful.

Research area: My research focuses on artificial intelligence, large language models, natural language processing, human–AI interaction, affective computing, and trustworthy AI. I study how AI systems generate, evaluate, and adapt human-facing responses in education, health communication, and research-support contexts. My work combines machine learning, LLM evaluation, user modeling, evidence-grounded response generation, and human-centred AI design. This project builds on my expertise in trustworthy LLMs, AI-supported research workflows, hallucination analysis, and transparent AI systems that support users while preserving human judgment and research integrity.

Student roles:
The intern will contribute to all stages of the 12-week research project under close faculty supervision. In the first stage, the student will review selected literature on large language models, retrieval-augmented generation, hallucination, citation grounding, research integrity, and human–AI interaction. The intern will help define the project scope and create a small set of synthetic literature-review tasks using public or instructor-provided research documents.In the second stage, the intern will design a claim-evidence taxonomy for LLM-generated research support. This taxonomy will classify responses according to whether claims are supported by the provided documents, partially supported, unsupported, overconfident, incomplete, missing evidence, or linked to incorrect citations. The intern will implement a proof-of-concept workflow in Python using structured prompts, retrieval-based evidence selection, LLM-generated responses, and simple evaluation scripts.
In the final stage, the intern will test the prototype on controlled research tasks, analyze successes and failure cases, and document limitations. The student will prepare summary tables, evaluation results, and visualizations, contribute to the final technical report, and present the project outcomes. The intern will not work with human participants, confidential data, student records, clinical decision-making, or production systems. The project is designed as a focused undergraduate research internship with clear weekly milestones, regular supervision, and concrete outputs suitable for future academic publication or graduate-level extension.

Skills required:
The ideal intern should have a background in computer science, artificial intelligence, data science, software engineering, information science, or a related field. Required skills include Python programming, basic machine learning or natural language processing, ability to read research papers, and clear written communication. Experience with large language models, retrieval-augmented generation, prompt engineering, APIs, data annotation, citation analysis, or evaluation metrics is an asset but not required. The student should be careful, organized, and interested in trustworthy AI, research integrity, evidence-grounded generation, and the evaluation of LLM-based research tools.

Research area: 1 - Network Security and Privacy. (we work on a wide range of problems in network security and privacy). 2 - Machine learning/ deep learning: Adversarial ML/DL, traffic classification, and cyber-attack classification. 3 - 5G security: Network slicing security, threats modelling, and security-related performance evaluation of 5G networks. 4 - Internet-of-Things (IoT): We investigate the Security of 6LoWPAN layer and also of routing in IoT networks.

Student roles:
Our group is a collaborative environment which is an excellent setup for experimentation with large language models, machine learning, deep learning, and open-source tools. First, the intern will be presented with some literature to study to gain enough background in the research topic, i.e., adversarial ML/DL, LLM use in network security. The intern will work directly with a group of graduate students to help them build and run experiments. They will be required to write code in support of the experiments, run experiments, collect data, and work on organizing the data in a presentable format. The intern will be expected to make presentations on their work on experiment setup, tools customization, and data collection and presentation. The intern (where appropriate) might participate in publishing the research results in a report or a paper.

Skills required:
Computer science or computer engineering.
Strong background in networking (one or two undergraduate courses).
Very strong software development skills.
Good understanding of Large Language Models (LLMs), Machine learning and Deep learning algorithms and their application
Desirable to have working knowledge of Python, and familiarity with LangChain, TensorFlow, Keras, PyTorch, Numpy, and Scikit-learn

Research area: My research area is primarily in evolutionary computation, with emphasis on cooperative–competitive frameworks and the design of memory and archive models that enhance adaptability, modularity, and long-term knowledge retention in complex learning systems. My research seeks to advance scalable and explainable approaches to artificial intelligence, particularly in settings where dynamic environments and multi-objective trade-offs must be addressed, such as image processing and medical applications under a streaming data context.

Student roles:
The student will work as part of a research team investigating how Evolutionary Computation can be used to automatically design better neural network architectures. The project focuses on creating AI models that are accurate, efficient, and easier to understand. Rather than manually designing neural networks, the student will help develop software that uses evolutionary algorithms to search for and evaluate different network designs.

The student will assist with implementing algorithms in Python, running machine learning experiments, and evaluating the performance of generated models. This will involve training neural networks, collecting and analyzing results, and comparing models based on factors such as accuracy, training time, model size, and complexity. The student will also help investigate methods for improving model interpretability and reducing unnecessary architectural complexity.

In addition to software development and experimentation, the student will conduct literature reviews to better understand current research in Neural Architecture Search, Evolutionary Computation, and Explainable Artificial Intelligence. The student will participate in regular meetings with the research team, discuss findings, and help identify new research directions and improvements to the developed methods.

The role will provide hands-on experience with Artificial Intelligence, Machine Learning, evolutionary algorithms, optimization, and research methodologies. Students will learn how to design experiments, evaluate scientific results, and communicate their findings through presentations, technical reports, and potentially research publications. By the end of the project, the student will have contributed to the development of a framework capable of automatically discovering neural network architectures that balance predictive performance, computational efficiency, and understandability. This experience will help prepare students for graduate studies or careers in Artificial Intelligence, data science, machine learning, and software development.

Skills required:
Students should have strong programming skills in Python and a solid foundation in computer science concepts such as algorithms, data structures, and software development. An interest in Artificial Intelligence, Machine Learning, or optimization techniques is essential. Prior experience with neural networks, PyTorch, TensorFlow, or data analysis is considered an asset but is not required. Students should be comfortable learning new technical concepts, reading research papers, and conducting experimental evaluations. The project is well suited for motivated students interested in AI research and provides opportunities to develop skills in evolutionary algorithms, neural architecture search, model interpretability, and scientific research methods.

Research area: Cybersecurity, machine learning, explainable artificial intelligence (XAI), anomaly detection, and network traffic analysis. This project focuses on developing transparent AI methods for intrusion detection systems that can identify suspicious network behaviour and explain, in human-readable form, why an alert was triggered.

Student roles:
The intern will support the design and implementation of an explainable AI framework for network anomaly detection. Main tasks include preparing public intrusion datasets, extracting and selecting interpretable traffic features, training and evaluating anomaly detection models, and integrating explainability methods that provide per-alert feature attribution. The student will also help create clear visual outputs that show why specific alerts were generated.

The intern will compare baseline models, analyze trade-offs between predictive performance and interpretability, and document results in a reproducible form. Depending on progress, the student may also help build a simple dashboard or prototype interface to demonstrate explainable intrusion alerts in a user-friendly way. The internship will conclude with a technical report, reproducible code, and publication-quality figures or tables suitable for a research paper.

Skills required:
computer science, cybersecurity, software engineering, data science, or computer engineering. Useful skills include Python, machine learning, data preprocessing, visualization, and basic knowledge of networking or intrusion detection. Familiarity with libraries such as scikit-learn, PyTorch/TensorFlow, SHAP, or LIME is helpful but not required.

Research area: Artificial Intelligence (AI), LLM, Deep Learning, Cyber-security, Internet of Things (IoT), Sensor/Data Fusion, Wireless communications networks, IoT platforms.

Student roles:
]Propose an architecture for the system, undertake the different design, programming and validation tasks for the different algorithms.

Skills required:
AI (deep learning and LLM)
programming skills.
wireless communication networks.
Computer networks
microprocessors
Cyber-security

Research area: My specialized research area is Responsible AI, with a focus on explainability, interpretability, reliability, privacy, and control of modern machine learning and large language model systems. My work investigates how complex AI models make decisions, how their predictions can be explained in data-centric and model-centric ways, and how their behavior can be evaluated and improved for trustworthy deployment. I am particularly interested in interpretable and aligned LLM-augmented systems, including retrieval-augmented generation and agentic AI, where transparency, safety, and robustness are essential. My broader goal is to develop practical methods that make AI systems understandable, reliable, and accountable in real-world applications.

Student roles:
The student will contribute as a research collaborator within a clearly scoped 12-week project on explainable inference-time steering of large language models. The student will not be expected to design a complete alignment system independently; instead, they will work under close supervision on a focused component of the project.

In the first stage, the student will complete a guided literature review on representation steering, mechanistic interpretability, responsible AI, and inference-time alignment. They will summarize a small number of key papers and help identify practical methods that can be implemented within the internship timeline.

In the second stage, the student will help set up the experimental environment using existing open-source LLMs and libraries. Their role will include running baseline prompting or steering methods, preparing small evaluation examples, and documenting the experimental setup so that results are reproducible.

In the third stage, the student will conduct controlled experiments to examine whether selected inference-time interventions improve policy compliance, reduce unsafe or privacy-sensitive outputs, and preserve response usefulness. They will also help analyze which layers, tokens, or representation patterns appear most relevant to the steering behaviour, supporting the explainability goal of the project.

In the final stage, the student will organize results into a short technical report, prepare figures or tables, and contribute to future research directions. Throughout the internship, the student will participate in weekly meetings, receive structured mentoring, and work with staged deliverables. The expected outcome is a preliminary experimental study and reproducible codebase, not a finished production system. This role is designed to give the student meaningful hands-on research experience while keeping the workload achievable and well supported.

Skills required:
The student should have a strong background in computer science, artificial intelligence, or a related field, with solid programming skills in Python and familiarity with machine learning or deep learning frameworks such as PyTorch or Hugging Face Transformers. Prior exposure to natural language processing, large language models, prompt engineering would be valuable. The project also requires curiosity about explainability, interpretability, and AI safety, plus the ability to read research papers, run controlled experiments, analyze results, and communicate findings clearly. Experience with representation analysis, model evaluation, or open-source LLMs is helpful but not required.

Research area: I had many years of experience in providing statistical consulting to a broad range of clients and training students and researchers on the conduct of statistical analyses. My main research interests include longitudinal analysis and multilevel models, person-centered statistical approaches (latent class analysis and growth mixture models) for mental health program evaluation, structural equation models, and clinical trial design. I have cultivated a dynamic research consortium that is both innovative and inclusive, offering an enriching environment for the mentorship and development of Highly Qualified People (HQP).

Student roles:
The student will contribute to the development, implementation, and evaluation of statistical and machine learning models for frailty-related survival outcomes using UK Biobank data. A major responsibility will be to construct a frailty index based on accumulated health deficits, including chronic conditions, functional limitations, self-reported health, lifestyle factors, and available clinical or biomarker measures. Where repeat assessment data are available, the student will help organize longitudinal datasets and examine changes in frailty over time.
The student will assist with data cleaning, coding, harmonization of variables, missing data assessment, and preparation of analytic datasets linked to mortality and health outcome records. They will also conduct a focused literature review on frailty indices, longitudinal frailty trajectories, survival prediction, and explainable machine learning in aging research.

Under supervision, the student will implement traditional survival models, including Cox proportional hazards models, competing risks models, and landmark prediction models. They will also apply machine learning methods suitable for time-to-event and longitudinal data, such as random survival forests, gradient boosting survival models, XGBoost-based survival models, and neural network survival approaches where appropriate. The student will participate in feature selection, model tuning, validation, comparison, and interpretation.
Key evaluation tasks will include assessing model discrimination, calibration, time-dependent prediction accuracy, and clinical utility. The student will also generate interpretable outputs, including variable importance measures, SHAP summaries, risk profiles, and visualizations of predicted risk, to support clinical and public health interpretation.

The student will meet regularly with the supervisor and research team, present progress at research meetings, and contribute to reports, abstracts, manuscripts, or conference presentations. This role will provide hands-on training in longitudinal data analysis, survival modeling, machine learning, and applied health data science using a major international cohort resource.

Skills required:
The student should have training in any of Statistics, Preventive medicine, computer science or a related field. Essential skills include programming in R or Python, data management, regression modeling, and basic survival analysis. Experience with large cohort datasets, longitudinal data analysis, machine learning methods such as random forests, XGBoost, or neural networks, and health-related data is highly desirable. Familiarity with UK Biobank data, frailty, aging research, linked health records, and explainable AI would be an asset. The student should have strong analytical, communication, and teamwork skills and an interest in applying machine learning to aging and population health research.

Research area: My research interests are in the disciplines of software engineering and applied artificial intelligence, with applications in cybersecurity. I investigate AI-driven approaches to detect software vulnerabilities, secure the growing class of systems built on large language models, including code-generating assistants and autonomous AI agents, and identify AI-generated and human-driven social engineering attacks. My work addresses the security of AI systems themselves, including vulnerabilities introduced by LLM code generation and the emerging risks associated with agentic AI, alongside the use of AI to detect and counter malicious content and manipulation online.

Student roles:
1. Review literature on human trust calibration and AI confidence calibration under attack.
2. Design a human-subjects study protocol and obtain ethics approval.
3. Implement the AI system component with adversarial input and confidence logging.
4. Run the study and measure human trust alongside model confidence under adversarial conditions.
5. Analyze whether exposing model uncertainty helps or worsens trust calibration.

Skills required:
Disciplinary Background – Cognitive science, psychology, human-computer interaction, or computer science, with demonstrated interest in human-AI interaction
Quantitative Skills – Statistical competence sufficient to interpret calibration metrics and experimental results
Analytical Skills – Demonstrated capacity in experimental design and statistical analysis (R or Python)
Other Relevant Skills – Foundational understanding of machine learning confidence and calibration concepts; demonstrated willingness to navigate research ethics board (REB/IRB) processes

Research area: Our research in privacy-preserving machine learning addresses the growing challenge of training and deploying ML models on sensitive data without compromising individual privacy. We investigate three complementary paradigms: Federated Learning (FL), which enables collaborative model training across decentralized data sources without centralizing raw data; Differential Privacy (DP), which provides formal mathematical guarantees against information leakage by introducing calibrated noise into the learning process; and Homomorphic Encryption (HE), which allows computation directly on encrypted data, ensuring that neither models nor intermediate results are ever exposed in plaintext.

Student roles:
Students will join the CILS Laboratory at Acadia University and collaborate with other students working on related research topics. They will conduct a literature review to gain an understanding of the state of the art, implement and evaluate a predefined experimental pipeline, and analyze the obtained results. Their work is expected to contribute to an ongoing research effort and culminate in the preparation of a research paper.

Skills required:
The student will need to have some basic understanding in Deep Learning and Convolutional Neural Networks. Good English writing skills.

Research area: My research blends Engineering Design, Human-Computer Interaction, Cognitive Psychology, and Neuroscience of Creativity towards developing human-centered AI-driven methods and tools that align with human needs, strengths, and expectations. Methodologically, I use human-subject investigations of behavior and cognition involving psychological and neuroimaging measures, computational modeling, and cognitive theory-based AI tool development to study the impact of AI on human design ability. My work uniquely intersects a variety of traditional fields of research and contributes to increasing knowledge in human design behavior (psychology), human collaboration with AI (human-computer interaction), and new theories and methods to improve engineered product outcomes (mechanical engineering).

Student roles:
This project is expected to involve three main tasks: First, the project will require the exploration of foundational creativity and design theories (e.g., dual-process creativity theories) to guide the development of new GenAI-based methods and design interventions. The student will, based on a brief literature review, design a new intervention and describe its anticipated effects on overall design outcomes. For example, prior research may reveal specific characteristics of inspirational images that can be helpful for promoting idea novelty when shown to designers in real time. The student will be responsible for then prompt engineering various off-the-shelf multimodal large language models (MLLMs) to enable the new intervention (e.g., using MLLMs to generate inspirational images from the designer’s current sketch). To evaluate the effectiveness of the new method, the final project stage will involve human-subjects experiments and/or online studies, in which, for example, a simple design task (e.g., sketch or 3D-model-based) will be provided to participants, and the impact of the intervention against a control group will be assessed using statistical analysis. Depending on factors such as the current stage of the project and the student’s interests and skills, emphasis on one or more tasks will be placed accordingly. The student will be expected to communicate their methods and findings weekly (e.g., through one-on-one meetings) and over the course of the project in a written report, which may potentially lead to future publication.

Skills required:
Desired experience of students include: human subjects research (online or in-person surveys, interviews, experiments), statistical analysis/modeling, experience with LLMs or other machine learning models (e.g., interaction with backend API using Python), basic computer programming skills (Python preferred). Candidates will be hardworking critical thinkers who are self-motivated and have great communication skills.

Research area: My research group focuses on compiler optimization and automatic hardware generation for machine learning and high-performance computing. We develop techniques that automatically transform programs into efficient implementations for CPUs, GPUs, and specialized accelerators such as FPGAs. Current research topics include sparse and compressed tensor computations, equality-saturation-based optimization, domain-specific languages, and hardware accelerator generation. Our goal is to make advanced computing systems easier to program while improving performance and energy efficiency.

Student roles:
The student will investigate the relationship between sparse and compressed tensor representations and the efficiency of modern hardware accelerators such as GPUs and, if time permits, FPGAs. Their responsibilities will include reviewing relevant literature, implementing computational tensor kernels, collecting and characterizing workloads from machine learning and scientific computing applications, and evaluating metrics such as memory footprint, memory bandwidth requirements, computational intensity, and hardware utilization.

The student will develop analytical models and conduct experimental studies to better understand how different tensor representations interact with modern accelerator architectures, including GPUs and their tensor cores, and potentially FPGAs. They will participate in regular research meetings, present progress updates, and contribute to the analysis and interpretation of results.

Depending on project progress and interests, the student may also explore techniques for automatically selecting tensor representations that best match the characteristics of a target hardware platform. The student will work closely with the research team and may contribute to technical reports, open-source software artifacts, or research publications resulting from the project.

Throughout the project, the student will gain hands-on experience with sparse and compressed tensor representations, performance analysis, and the implementation of tensor computations on modern accelerator platforms.

Skills required:
The ideal candidate will have a background in computer science, computer engineering, or software engineering. Applicants should be comfortable with GPU programming and have a good foundation in linear algebra.

Basic knowledge of computer architecture, machine learning, or high-performance computing is desirable but not required. Familiarity with Linux-based development environments and version control systems (e.g., Git) is expected.

Research area: Wireless networks architecture, resource management architectures and protocols, modelling and performance evaluation of networked systems, combinatorial algorithms for graphs, scheduling, and network reliability analysis

Student roles:
Through weekly meetings with the supervisor, the student is expected to develop a solid understanding of the problem context, as well as the specific details of a selected problem within the Energy Harvesting Wireless Sensor Networks (EH-WSNs) domain. In the next phase, the student will formulate and refine ideas for a potential solution approach. This will be followed by implementation, performance evaluation, and systematic documentation of the results.

Skills required:
1. Problem solving skills, and desire to work in the area of computer networks
2. Ability to program in Matlab, Python and/or C/C++
3. Ability to work independently and explore new ideas
4. Experience with a large software system (e.g., network simulators) is an advantage, but not required

Research area: Dr. McArthur’s research focuses on improving the effectiveness of and access to rehabilitation for people living with chronic health conditions across the continuum of care, particularly home and long-term care. She is interested in the use of artificial intelligence to support fall and fracture prevention and improve healthy aging.

Student roles:
Students will be involved in preparing the research project, participant recruitment, data collection, analysis, and report writing associated with the proposed project. This includes preparing the case studies based on de-identified clinical data, contacting and communicating potential participants, conducting interviews with older adults, family caregivers, and health care professionals. Students will also be involved in working with the health care providers to review the cases and provide their risk assessment and rating of confidence through a structured survey. Students will then be involved in analyzing the qualitative and quantitative data and writing a final report.

Skills required:
Students will require:
- excellent written and oral communication skills
- excellent organizational skills
- strong critical thinking skills
- an interest in discovery
- independent problem solving skills
- qualitative and quantitative data analysis skills are an asset

Research area: My main research area is mobile and wireless networks. More specifically I work on cognitive radio networks, machine learning-based solutions for wireless networks, multiple input multiple output (MIMO) systems, cybersecurity, and sixth generation (6G) networks. Based on my vast experience in cognitive radio networks (as it was my PhD dissertation topic), I am recently exploring its potential with intelligent reflecting surfaces.

Student roles:
Both the students will work together to understand CRNs, typical machine and deep learning models, and agentic AI. Later, Student 1 will focus on developing mathematical models while Student 2 will build simulations. At the final phase of the project, both students will compare analytical and simulation results and write a conference/journal paper.

Skills required:
Students should have knowledge about R&D. Strong Python programming and mathematical optimization skills are required.

Research area: This is a project in Human-Computer Interaction and Human-AI Interaction: 1) Human-Computer Interaction (HCI) focuses on the design, implementation, and evaluation of interactive systems. It often involves developing new technologies and then evaluating them with users; 2) This project is specifically focusing on "Human-AI Interaction". Here, we ask the question "Is chatting the best way to interact with an AI?". Thus, we explore alternative approaches to interact with AIs such as Large Language Models (LLM).

Student roles:
1) Developing an interactive system that integrates AI; 2) Conducting controlled experiments to evaluate the system developed. Depending on the advancement of the project, the student may also participate in analyzing data and writing a research paper

Skills required:
1) Good programming skills (likely in JavaScript/TypeScript + React, but this is open to discussion); 2) Experience with using AI (either via an API or by training models) is a plus, but not required; 3) Experience or interest in creative work (writing, drawing, etc) is a plus, but not required

Research area: My long term research vision is to use information system technologies to make learning and education more adaptive, intelligent and personalized, in order to better support learners and address some of the prevalent challenges in online learning, such as reducing drop-out rates and increasing learner satisfaction. This research project is in the field of educational technology and user-adaptive systems, utilizing different areas such as adaptivity and personalization, learning analytics, game-based learning, motivational techniques, user modelling, human-computer interaction, and others.

Student roles:
In this undergraduate project, the students will be part of the project team of the larger project. The undergraduate students will work on one or few features/tasks of the above-described larger project, dealing with the design, development and/or evaluation of game features, components, algorithms and/or mechanisms. For each task, the students will:
1. Conduct a small literature review to investigate current research and/or common practices for the respective task;
2. Design a concept for the implementation of the respective task;
3. Implement the respective task;
4. Test and evaluate the respective task;
5. Provide documentation and a user guide for the respective task.

The students are expected to attend research meetings. Furthermore, the students (together with the project team) and with the support of the supervisor are expected to write a scientific publication about their research and submit it to a high-quality conference/journal.

Skills required:
Required skills/background:
• Enrollment in an undergraduate program in Computer Science, Information Systems or a related field
• Strong programming skills (e.g., Java, C++, etc.) and/or strong web programming skills (e.g., PHP).

Assets:
(These assets are highly depending on the part of the project you will be working on. Having some of these assets will be helpful for certain parts):
Experience in
• doing research
• designing and building tools or systems
• designing user interfaces and data visualizations
• game design and game development
• analyzing data
• building artificial intelligence / data mining algorithms

Research area: My specialized research interests lie in Software Engineering and Software Quality. Through my research, I aim to facilitate software development, maintenance, and evolution using machine learning and develop new methods and techniques. More specifically, I focus on analyzing software artifacts (e.g., design, source code, documentation, requirements, etc.) using machine learning and natural language processing techniques, assessing the quality of software design and implementation, analyzing and prioritizing software requirements, and managing technical debts or issues.

Student roles:
- Write a parser to parse source code (Python/Java)
- Extract emotions-related concepts from the source code
- Analyze findings and derive conclusions based on various statistics
- Write a final report with research questions answered

Skills required:
- Strong programming skills in Java/Python
- Source code parsing. In particular, Java, Python, or JavaScript
- At least NLP library in Java (OpenNLP, StanfordNLP, or MALLET) or Python (NLTK, CoreNLP, or scikit-learn)

Research area: The research area for this project is a type of geometrically defined graph which has numerous applications in network science to communication network problems and geometric covering problems. A graph is a collection of vertices (or nodes) and edges that join pairs of vertices. In network of transmitter/receivers, each node/vertex is placed at a point in the plane and can communicate with other nodes within a fixed distance. When this distance is 1, the graph is called a 'unit disk graph'.

Student roles:
The student will be working in a team with other undergraduate student researchers (possibly including other Mitacs interns and local students), graduate students, and the supervisor. The group will meet weekly to discuss progress and plan the upcoming work. For the first couple of weeks, the weekly meetings will also include lectures by the supervisor (and possibly graduate students) on the required background for the topic.

Within the scope of the planned research, students will have some flexibility in choosing the parts of the research project that most interest them. Possible tasks include reading research papers and trying to adapt techniques therein to our project, using mathematical software to numerically solve optimization problems, and conducting computer searches to identify optimal node placements.

The group will use online collaboration tools to stay in regular contact throughout the week and the supervisor will be available for discussions outside of the planned group meetings.

Students will be required to maintain an ongoing report of their work, typeset using LaTeX, that will be compiled into a final report summarizing the progress at the end of the internship. Students will also be involved in the collaborative work of producing research papers on the outcome of the project to be submitted for publication.

Students will have the opportunity to participate in professional development programs run by the Faculty of Science to support undergraduate student researchers and may have the opportunity to participate in either a poster competition or lightening-talk event at the end of the summer. These events will also provide opportunities for interns to network with other student researchers in the department and the Faculty of Science.

Skills required:
Students working on this project are required to have experience with graph theory and combinatorics. An introductory course covering these topics would be sufficient. Experience with mathematical software (Sage, Mathematica, Maple, etc.) would be an asset, but is not required. A final report on the project will be written using LaTeX and so previous exposure to using LaTeX is an asset, but students with no experience would be taught.

Research area: In general, my research lies within Software Engineering and Computer Systems, with special interests in software analytics, software performance engineering, source code analysis, software architectural recovery, software visualizations and debugging and monitoring of distributed systems. In particular, I strive to conduct practical research, which provides contributions to both academia and practice in the area of designing, developing, testing and maintaining large-scale software systems.

Student roles:
The selected MITACS student(s) will be the primary person responsible for this project. The student(s) will gain first-hand experience in this very important intersection area between AI and Software Engineering, which has great values in both research and practice.

Skills required:
- Excellent analytical and communication skills

- Knowledge in AI and ML

- Comfortable with programming and data analysis

Research area: Wassim Bouachir is a professor of computer science at TÉLUQ (University of Québec). His research work focuses on the development of novel machine learning and computer vision methods for a wide range of applications, such as security, environment sciences, and health-care systems.

Student roles:
The student will start by studying the existing 1D-CNN model, designed to detect Parkinson's disease from gait signals. He/she will then focus on adapting the neural network to improve the accuracy of severity prediction according to the Unified Parkinson's Disease Rating Scale (UPDRS). The source codes and datasets for this project are available. A brief description of our previous work is available at: https://www.uquebec.ca/reseau/fr/medias/actualites-du-reseau/detecter-la-maladie-de-parkinson-grace-lintelligence-artificielle

Skills required:
We are looking for a candidate with an understanding of machine learning and good programming skills in Python. He/she will use Python implementations of machine learning libraries (e.g. PyTorch, TensorFlow).

Through the project, the intern will gain experience in designing deep learning models for 1D signal processing. This includes 1D-CNNs and Transformer networks.

Research area: Reconfigurable structures inspired by biological systems offer a promising approach for creating lightweight adaptive systems capable of changing shape while maintaining structural integrity. This research focuses on the design, additive manufacturing, and experimental characterization of multistable structures fabricated from polymer composites and smart materials. The project investigates how geometric design, material selection, and embedded actuation mechanisms influence shape transformation, energy efficiency, and long-term durability. The outcomes will contribute to the development of next-generation adaptive structures for aerospace, robotics, and advanced engineering applications.

Student roles:
Fabrication using polymer and multi-material 3D printing systems.
Conduct snap-through and cyclic loading experiments.
Set up displacement, force, temperature and actuation measurements.
Process experimental data and compare with numerical predictions.
Assist with embedding and testing shape memory alloy actuators.
Research Dissemination
Prepare reports and figures

Skills required:
1. One or more of the following skills are desirable:
2. Experience with additive manufacturing and 3D printing technologies.
3. Experimental mechanics and mechanical testing.
4. Data processing and analysis
5. Knowledge of smart materials, shape memory alloys, or adaptive/multistable structures is an asset.
6. Laboratory experience and ability to work independently.

Research area: I specialize in computer graphics, virtual humans, crowd simulation, and immersive virtual reality. My research focuses on building realistic and interactive digital environments where virtual characters can perceive, move, communicate, and respond to users in socially believable ways. This includes emotion-aware avatars, pedestrian and crowd behaviour, embodied interaction in VR, real-time animation, and simulation-based evaluation of human movement and social spacing. The broader goal of my work is to create intelligent virtual environments for games, film-making, training, digital twins, education, and human-centred simulation.

Student roles:
The student will help design, implement, and test a prototype system for emotionally responsive virtual humans. At the beginning of the internship, the student will review related examples of facial expression animation, attention models, and emotional behaviour in virtual characters. Based on this review, the student will help define a small set of facial expressions, body cues, and scene events that the virtual humans should respond to.

The student will implement an attention model that estimates whether a virtual character can see nearby people, objects, or events. This model will use simple information such as the character's viewing direction, field of view, distance, and the importance of the event. The student will then connect this attention model to an emotion selection system that chooses an appropriate facial expression using rule-based or probability-based logic.

The student will also integrate facial expressions with body cues such as gaze shifts, head movement, posture changes, and small gestures. These behaviours will be demonstrated in short interactive scenarios involving small groups of virtual humans. Possible scenarios include a character reacting with surprise to sudden movement, showing discomfort when another character comes too close, or expressing happiness during a friendly interaction.

The student will assist with evaluating the system using both objective and subjective measures. Objective measures may include reaction timing, movement changes, interpersonal spacing, and avoidance behaviour. Subjective measures may include user feedback on whether the characters appear natural, socially believable, comfortable, and engaging.

By the end of the 12-week internship, the student is expected to produce a working Unity or Unreal prototype, documented code, demo videos, a short technical report, and initial evaluation results.

Skills required:
The student should have a background in computer science, game development, computer graphics, animation, artificial intelligence, HCI, or a related area. Experience with Unity or Unreal Engine is highly desirable. Programming experience in C# / C++ is expected (depending on whether the student will use Unity or Unreal). Familiarity with 3D avatars, character animation, motion database, blend shapes, or animation clips is a huge asset. The student should be comfortable building interactive prototypes and interested in virtual humans, games, animation, or immersive environments.

Research area: Dr. Nasir leads the Research in Artificial Intelligence and Data Analytics for Real-World Solutions, conducting interdisciplinary research that integrates AI, Machine Learning, Generative AI, and Quantum Computing to advance intelligent recommendation systems and address complex real-world challenges.

Student roles:
The student will be responsible for designing, implementing, and evaluating a fairness-aware recommendation system that utilizes quantum-inspired optimization techniques to promote diverse and equitable product discovery. The project will begin with a literature review of recommendation systems, fairness in AI, popularity bias, and quantum-inspired optimization methods. The student will investigate how traditional recommendation algorithms may unintentionally favor popular products, sellers, or categories and identify strategies to mitigate these biases.

Using publicly available datasets, the student will preprocess user-product interaction data and develop baseline recommendation models for comparison. The student will then formulate the recommendation task as a multi-objective optimization problem that balances recommendation relevance with fairness objectives such as diversity, coverage, and equitable exposure. Quantum-inspired optimization approaches, including QUBO formulations and simulated quantum annealing, will be explored to generate recommendation lists that satisfy both accuracy and fairness requirements.

The student will conduct experiments to evaluate the effectiveness of the proposed approach using recommendation accuracy metrics alongside fairness measures. Results will be analyzed to understand trade-offs between personalization and fairness, and to identify practical strategies for developing responsible recommendation systems. Throughout the project, the student will document methodologies, prepare technical reports, present findings, and contribute to the development of a research paper suitable for submission to an IEEE-style undergraduate research conference.

Skills required:
Students participating in this project should have basic programming skills in Python and a foundational understanding of machine learning and data analytics. Familiarity with recommendation systems, data preprocessing, and model evaluation techniques is desirable. Knowledge of fairness concepts in AI, such as bias mitigation and diversity metrics, would be beneficial but can be learned during the project. Students should be interested in responsible AI, optimization techniques, and emerging quantum technologies. Strong analytical thinking, problem-solving abilities, and a willingness to explore interdisciplinary research topics are important for successfully completing the project.

Research area: My specialized research area focuses on deep learning–based fake news detection, with an emphasis on developing ensemble models that integrate heterogeneous architectures such as LSTM and CNN variants. I also work on metaheuristic optimization algorithms to enhance model accuracy and stability. Additionally, my research examines cross‑domain generalization challenges in misinformation detection and explores strategies to improve model robustness across diverse news sources.

Student roles:
The student is expected to know some skills of programming like Matlab or/and Python, or R and swarm intelligence algorithms (is able to learn it quickly if s/he does not know). Student will write many computer programs by using Matlab software or Java and he will apply swarm intelligence algorithms such as Genetic algorithm,
Particle Swarm Optimization Algorithm, differential evolution algorithm, spider, bat, and other algorithms. student will compare these algorithms with other algorithms. Also, student will help in writing a report and graph as a research assistant.

Skills required:
Writing extensive computer programs in Matlab or Python, or R, Knowing swarm intelligence algorithms and optimization will be assets.

Research area: My research focuses on trustworthy AI and machine learning for cybersecurity in critical infrastructure. I work on privacy-preserving machine learning particularly federated learning, which enables organizations to train models collaboratively without sharing sensitive operational data alongside anomaly and intrusion detection, AI security, and responsible AI governance in regulated environments. My broader interests span IoT, pervasive systems, and autonomous systems safety. Combining enterprise AI leadership with academic research, I supervise projects that produce reproducible benchmarks and publishable results while giving students direct exposure to the security challenges facing real industrial and critical-infrastructure operators.

Student roles:
The intern will be the lead experimentalist.
Weeks 1–4: harmonize the public ICS datasets, establish centralized (non-federated) baseline models, and reproduce one published result.
Weeks 5–8: implement the federated pipeline in Flower, run experiments across heterogeneity settings, and compare aggregation strategies.
Weeks 9–12: conduct poisoning-robustness experiments, analyze and visualize results, package the reproducible benchmark, and co-author the paper draft.
The student will participate in weekly supervision meetings, maintain version-controlled experiment code, and present final results to the research group.

Skills required:
Strong Python skills and hands-on experience with a machine learning framework (PyTorch or TensorFlow) from coursework or projects. Solid foundations in machine learning: training loops, evaluation metrics, overfitting, and class imbalance. Comfort with preprocessing real-world time-series or tabular data. Prior exposure to federated learning, anomaly detection, or cybersecurity is an asset, but not required; these skills will be developed during the internship. The ability to design structured experiments and document results rigorously is essential. Compute resources for all experiments will be provided by the supervisor.

Research area: In the last few years, I have been working on communication networking aspects of resource management in 5G/6G and UAV assisted networks with emphasis on algorithm design, network architecture development, and system-level performance analysis. I, along with my students have designed novel machine learning algorithms and communication protocols for UAV and IoT networks, that have both (i) enhanced network performance, in terms of spectrum efficiency, coverage, and energy efficiency, and (ii) satisfied a wide range of cellular IoT devices' requirements and constraints.

Student roles:
The student will collaborate closely with an MSc student to design distributed cooperation and coordination protocols for FL-enabled wireless communication systems.
The student’s responsibilities will include:
• Assisting in the development and implementation of federated learning algorithms
• Contributing to the design of distributed resource management strategies
• Implementing and testing the proposed framework using simulation tools such as SUMO and Python-based machine learning models
• Analyzing performance metrics such as convergence behavior, spectral efficiency, and communication overhead
• Participating in research discussions, documentation, and potential publication preparation
Depending on progress and research outcomes, the student may also contribute to extending the simulation framework and evaluating advanced communication-aware FL techniques.

Skills required:
Students applying for this project should have:
• A solid background in computer networks
• Strong programming skills (preferably in Python)
Preference will be given to students who have completed coursework in:
• Wireless communication
• Machine learning

Research area: My specialized research area lies at the intersection of Artificial Intelligence and Intelligent Transportation Systems (ITS), focusing on developing advanced AI-driven methodologies to optimize urban mobility and enhance transportation safety, efficiency, and sustainability. I leverage Machine Learning, including Large Language Models (LLMs) and Federated Learning, to address complex challenges such as trajectory optimization, spatial data consistency, digital twins, map-matching, and indoor-outdoor navigation. My research aims to foster innovative solutions for connected, autonomous, and electric shared vehicles, significantly contributing to smarter, more resilient urban environments.

Student roles:
The student will play a key role in designing and developing a privacy-preserving federated learning (FL) framework tailored for decentralized spatiotemporal data modeling within Intelligent Transportation Systems (ITS), especially targeting the emerging landscape of connected, autonomous, and electric shared vehicles.

Their primary responsibilities will include conducting an in-depth literature review on federated learning architectures, privacy-preserving methods (e.g., differential privacy, secure multiparty computation), and spatiotemporal data modeling techniques. The student will acquire, clean, and preprocess real-world or synthetic datasets that reflect diverse mobility patterns in urban transportation scenarios. They will engineer relevant spatial and temporal features and structure the data to be used in the FL pipeline.

Technically, the student will design, implement, and train federated learning models using tools such as TensorFlow Federated/Flower/Flare, optimizing model architecture and communication strategies for performance under non-IID (non-independent and identically distributed) data conditions. They will compare FL models to centralized baselines to evaluate metrics such as prediction accuracy, robustness, and communication efficiency.

The student will also document all research experiments, contribute to project reports, and co-author academic publications and presentations. They are expected to actively participate in project meetings, share insights, and work collaboratively with researchers and industry stakeholders.

The role requires proficiency in Python, strong foundations in machine learning, and the ability to understand real-world challenges in ITS. Prior experience with federated learning, spatiotemporal data, or transportation datasets is an asset. This project will provide the student with valuable applied research experience at the intersection of AI and urban mobility, equipping them with skills relevant to both academia and industry.

Skills required:
Academic Background: Computer Science, Applied Computing, Data Science, Electrical and Computer Engineering, or related disciplines.
Strong foundational knowledge in Machine Learning, Deep Learning, and Data Analytics. Proficiency with Python programming and familiarity with relevant libraries/frameworks (TensorFlow, PyTorch, Scikit-learn). Prior experience or familiarity with Federated Learning techniques, frameworks, or platforms is preferred. Basic understanding or interest in Intelligent Transportation Systems (ITS), connected and autonomous vehicles, and urban mobility challenges. Capability to independently perform literature reviews.

Research area: My research focuses on AI-driven cybersecurity for Internet of Things (IoT) and cyber-physical systems. My work combines machine learning, federated learning, explainable AI, and intent-aware analysis to detect and mitigate cyberattacks in distributed IoT environments. I develop intelligent and privacy-preserving defence frameworks using real IoT testbeds consisting of Raspberry Pi, sensors, RFID systems, and edge devices. My research also explores lightweight AI techniques, honeypot-based defence, and LLM-assisted security analysis for scalable and trustworthy monitoring of smart infrastructure and critical systems.

Student roles:
The student will participate in developing an AI-driven cybersecurity framework for IoT networks, spanning the full research lifecycle from testbed configuration and data collection through model implementation, experimental evaluation, and formal reporting.

Core responsibilities include:
• Configuring and monitoring heterogeneous IoT devices (Raspberry Pi systems, Pico W microcontrollers, environmental sensors, RFID readers, and smart bulbs) within the laboratory testbed
• Collecting, labelling, and preprocessing network traffic data generated under both normal operation and cyberattack scenarios
• Implementing a federated learning-based anomaly detection model in Python
• Applying intent-aware behavioural analysis techniques for anomalous device activity identification
• Integrating lightweight LLM-based components for contextual behavioural interpretation
• Evaluating detection performance using standard cybersecurity metrics: accuracy, precision, recall, and F1-score
• Conducting comparative experimental analysis of centralized and federated detection approaches
• Preparing technical documentation, a final research report, and a formal presentation of findings

The 12-week research plan is structured as follows:
• Weeks 1–2: IoT security fundamentals, federated learning literature review, testbed familiarization
• Weeks 3–4: Baseline anomaly detection implementation and data preprocessing pipeline development
• Weeks 5–6: Federated learning framework development and decentralized training setup
• Weeks 7–8: Live data collection and network traffic generation from the IoT testbed
• Weeks 9–10: Intent-aware analysis development and LLM-assisted behavioural interpretation
• Week 11: Experimental evaluation and comparative analysis
• Week 12: Final report preparation, research presentation, and future directions discussion

The intern will participate in regular weekly supervisory meetings throughout the internship. Interns showing strong research performance will be considered for co-authorship on a resulting peer-reviewed publication.

Skills required:
• Basic to intermediate Python programming
• Introductory understanding of machine learning concepts (classification, model evaluation)
• Interest in at least one of: cybersecurity, IoT systems, artificial intelligence, or network analysis
• Exposure to Linux command-line environments or basic networking concepts is beneficial but not required
• Motivation and independence to work in a hands-on experimental research environment
• Willingness to learn new technologies and participate in collaborative research activities
• Strong analytical thinking, problem-solving, and communication skills are considered an asset

Research area: My research focuses on applying signal processing, machine learning, and statistical modeling to complex time-series data. Drawing on experience with biosignals such as electroencephalographic (EEG) recordings, I develop methods for feature extraction, noise reduction, pattern recognition, and predictive modeling. Current interests include the analysis of financial time-series data, where signal-processing techniques and machine learning algorithms are used to identify market trends, detect regime changes, and improve forecasting performance.

Student roles:
The student will contribute to the development and evaluation of machine learning models for financial time-series analysis. Responsibilities may include collecting and preprocessing financial datasets, conducting exploratory data analysis, implementing signal-processing techniques for feature extraction and noise reduction, and developing predictive models using machine learning methods.

The student will assist in designing experiments, evaluating model performance, and interpreting results. They will be expected to document their work, participate in regular research meetings, and communicate findings through reports and presentations. Depending on project progress, the student may also investigate advanced topics such as deep learning architectures, market regime detection, feature importance analysis, or backtesting forecasting strategies.

The position offers an opportunity to gain practical experience in machine learning, data analytics, signal processing, and quantitative research while contributing to an active research project. Students will receive mentorship in research methodology, scientific communication, and computational analysis techniques.

Skills required:
Students should have a background in engineering, computer science, mathematics, statistics, data science, or a related field. Familiarity with Python programming and basic machine learning concepts is preferred. Experience with data analysis, signal processing, statistics, or time-series analysis is an asset but not required. Students should be comfortable working with datasets, learning new computational tools, and reading technical literature. Strong analytical and problem-solving skills are important.

Research area: While nuclear structure theory has historically been developed in the context of simple models, groundbreaking efforts are emerging to develop first-principles (or ab initio) many-body theories and simulations for all atomic nuclei, starting from only the fundamental nuclear and electroweak forces of nature. This has brought first ab initio predictions for even the heaviest systems; exotic nuclei, which exist at the very limits of stability and are crucial for understanding the origin of heavy elements in the universe; as well as fundamental quantities vital for worldwide searches aiming to discover new physics beyond the standard model with atomic nuclei.

Student roles:
The student will perform calculations, organize and interpret results, and work on implementing or testing extensions to existing codes. The student will also be encouraged to take part in group and lab-wide activities, such as colloquia, seminars, and journal clubs, and to interact with experimental colleagues, with whom they will have daily contact on-site at TRIUMF, as well as external collaborators at leading institutions worldwide (eg, MIT, Caltech, CERN, etc). There will also be ample opportunities to further hone scientific writing and presentation skills.

Skills required:
With the latest advances in high-performance computing and optimization algorithms at her/his disposal, the student will gain experience with parallel-computing techniques such as OpenMP or MPI and work on some of the world’s largest supercomputers. The ideal applicant will have mastered material encountered in introductory quantum mechanics courses and be familiar with concepts such as eigenvalues, wavefunctions, operators, unitary transformations, and angular-momentum algebra. Furthermore, some computing experience is required, with competency in Python and some experience with C++ would be helpful.

Research area: I have conducted extensive research in human mobility with geospatial data mining, including trajectory computing, mobility pattern discovery, travel behavior prediction and route planning, resulting in several publications in top journals and conferences. My long-term research goal is to advance the understanding of human mobility patterns and their interactions with environmental, social and transportation systems in both urban and marine contexts to support sustainable and smart mobility solutions. Achieving this goal requires the development of advanced geospatial artificial intelligence methods of modeling and optimizing human mobility.

Student roles:
The student will participating in data manipulation and modeling. The student will gain practical experience in preprocessing spatial trajectory data, including outlier removal, trajectory simplification and segmentation. In addition, the student will learn to use spatial databases for efficient storage and retrieval of trajectory data. The student will also become proficient in using GIS tools to visualize human movement trajectories effectively. Given the data-intensive nature of this research, the student will acquire essential skills in trajectory data modeling. Specifically, the student will receive training in in deep learning techniques, including encoder-based representation learning and generative adversarial networks (GANs), for extracting meaningful trajectory features and classifying vessel behaviors from massive trajectory datasets.

Skills required:
The ideal candidate should have a background in geographic information systems (GIS), computer science, data science, or a related discipline. Experience with programming, particularly in Python, is essential for data processing and model development. The student should have a strong interest in human mobility research and spatial data analytics, along with a foundational understanding of geospatial data mining concepts. The student should possess strong problem-solving skills and be motivated to work with large-scale trajectory datasets in an interdisciplinary research environment.

Research area: My research focuses on programming languages, in particular functional programming, type systems, semantics, logics, as well as their applications in broader domains.

Student roles:
- Participate in the formalism of language semantics and type systems
- Prove desirable properties, using pen & paper proofs and/or in a proof assistant
- Contribute to the implementation of a compiler and/or an interpreter
- Write papers and give research presentations

Skills required:
- Familiarities with programming languages, type systems, and formal semantics.
Example textbook: "Types and Programming Languages" (Pierce 2002)
- Familiarities with functional programming, such as languages like Haskell, OCaml, and Scala.
- Some familiarities with proof assistants will be a bonus, such as languages like Lean and Rocq.

Research area: My research is at the crossroads of software engineering, requirements engineering, and formal methods. Software-based systems continue to grow in complexity. A powerful way to handle complexity is through layers of abstraction. My group investigates abstract, declarative modelling as a new paradigm for expressing the desired, user-visible behaviour of the system. We study what abstractions to capture in language constructs; how to analyze the system formally and automatically with the best performance optimizations; how to use the results of the analysis to iteratively improve the model; and methodology and education for this new modelling paradigm.

Student roles:
The student will work the supervisor and graduate students to develop algorithms, implementations, and do benchmark performance analysis. The student will be involved in group and individual meetings multiple times a week. Implementation will be done in Java. The student is expected to provide thorough unit and integration tests of the developed code, and use version control systems (github), and participate in code reviews regularly. In addition to in-person meetings, research group communication is accomplished through Slack and email. If the research is successful, the supervisor would work with the student to prepare a paper submission to a conference or journal. The student should prepare a report and present their work at the end of their internship. The student will be provided with an office (likely in the WatForm lab with the graduate students) and equipment if needed. We have servers for performance analysis, but most work is done on one's personal computer. Finally, the student will be invited to join in social gatherings such as group lunches/dinners, and local activities.

Skills required:
1) undergraduate course in discrete math/logic. A basic knowledge of propositional and predicate logic.
2) knowledge of programming, in particular Java
3) undergraduate course in software engineering. A basic knowledge of the phases of software development, in particular, requirements engineering, and expressing 'what' a software-based system should do rather than its implementation, and unit and integration testing.
4) knowledge of version control systems such as git

Research area: My team works in Artificial Intelligence and Machine Learning. Specifically, we focus on Deep Learning, Reinforcement Learning, Lifelong Learning, Optimization, Foundation Models, and AI for Science. For more details, please check out the lab website: https://chandar-lab.github.io/

Student roles:
Review existing advances in LLMs; implement them for biology data; work on the drug discovery pipeline; implement metrics for evaluation and benchmarking of developed foundation models.

Skills required:
Strong background in deep learning and large language models; strong coding skills; proficiency in Python and PyTorch; knowledge of multi-GPU and distributed training;

A background in biology is desirable but not mandatory. You will be working on ML side of the project in collaboration with other researchers who have the biology background.

Research area: My research focuses on efficient foundation models and transfer learning. I develop machine learning algorithms that can reuse knowledge across tasks, domains, and data modalities, with an emphasis on in-context learning, and scalable model design. The goal is to build models that can adapt quickly to new scientific problems with limited labeled data and limited computation. I am particularly interested in applications in scientific problems, where data are often heterogeneous, noisy, expensive to collect, and distributed across experiments.

Student roles:
The successful candidate will be responsible for:

- Doing a literature review in the related areas
- Implementation of novel active learning and transfer learning algorithms based on our theoretical framework
- Contributing to efficient interdisciplinary research team collaboration
- Completing at least one of the objectives listed in the project description
- Writing a scientific report/paper

While the start date is flexible, in previous years, the selected students would work with me remotely once they were matched. Ideally, the project will result in a paper published in top venues (e.g., ICML, NeurIPS, ICLR).

Skills required:
You may be a strong candidate if you have (at least three of the following):
- a strong background in statistics, linear algebra
- strong motivation and interest in machine learning research.
- knowledge of machine learning, optimization
- published academic work on the topics of machine learning
- sufficient programming experience (e.g., Python)
- some experiences on advanced machine learning paradigms (e.g., computer vision, adversarial examples, watermarking, etc.)
- good English reading and speaking skills

Disabled applicants will receive priority in case they have equal qualifications.

Research area: My long-term research objective focuses on intra-operative target tracking for radiotherapy. In fact, real-time assessment and feedback enable clinicians to make necessary adjustments and improve the outcome of the procedure. I have a decade of experience in biomedical computing and medical devices product research, development and management. Before becoming an assistant professor at Bishop’s University and then at Toronto Metropolitan University, I used to work for a leading company in the radiotherapy industry. My research goals focused on organ tracking using medical images.

Student roles:
Develop and implement methods.
Apply the methods on datasets.
Analyze and document results.
Write conference/ journal paper.

Skills required:
Knowledge of Python.
Strong logic and fundamentals in statistics

Research area: My research focuses on safe and trustworthy AI for regulated and critical-infrastructure environments. I work at the intersection of AI governance, AI security, and autonomous software systems: how agentic AI can be deployed with verifiable controls, how machine learning can be secured and audited in high-stakes settings, and how privacy-preserving techniques such as federated learning enable collaboration across organizations that cannot share data. My work combines applied research with enterprise practice, including the Model Context Protocol (MCP), policy-as-code controls, audit evidence generation, and risk-based AI lifecycle governance aligned with frameworks such as NIST AI RMF and ISO/IEC 42001.

Student roles:
The intern will own the design and implementation of the evidence pipeline.
Weeks 1–4: study agent trace formats and the relevant control frameworks with supervisor guidance, then produce the evidence schema and control-mapping table.
Weeks 5–8: build the working trace-to-evidence pipeline and run it against three representative agentic workflows. Weeks 9–12: evaluate control coverage, classify controls as automatable / partially automatable / judgment-dependent, prepare the open-source release, and co-author the paper draft.
The student will join weekly supervision meetings, keep a structured research log, and deliver a final presentation and documented codebase.

Skills required:
Solid Python programming and data engineering skills (JSON processing, schema design, building data pipelines). Interest in the intersection of AI engineering and governance, auditability, or compliance, prior knowledge of standards like NIST AI RMF or ISO 42001 is not expected and will be taught. Familiarity with LLM agent frameworks or APIs is an asset, as well as strong analytical writing skills, since the project includes mapping technical artifacts to control requirements. Attention to detail and interest in trustworthy and responsible AI are essential.

Research area: Virtual locomotion is the technology that enables users to move from one place to another within a virtual environment. The teleportation technique has been acknowledged as one of the most preferred and effective techniques, which has been widely adopted in virtual reality (VR) applications. Although travel in multi-scale environments is often not considered the default use case for VR navigation, it is crucial to a wide range of serious application cases like astronomy, geography, biology, architecture, manufacturing, and teleoperation. Related work has shown that navigation across multiple scales can provide users with various insights that conventional navigation techniques cannot offer.

Student roles:
The students will become part of our XR research team and work with me and graduate students on our area of research. The students will mainly work on developing the software in Unity with C#. The students will also collaboratively design user studies and, if time permits, conduct the studies, analyze the collected data and document the results. In addition, we hope that the student will have a great time visiting Calgary and the Canadian Rockies that are close.

Skills required:
The students need to have good software development skills. The students should be keen on software development using Unity. Some experience with Unity or any other game engine is preferred, but this could also be acquired as part of the project. A curiosity for new Extended Reality (XR) technologies and software development is the key.

Research area: My research focuses on 3D computer vision, machine learning, and geometric deep learning for understanding large-scale 3D environments. I develop algorithms that enable computers to analyze, classify, segment, compress, and reconstruct 3D point clouds acquired from LiDAR scanners and other sensing technologies. Current research themes include semantic understanding of urban scenes, topology-aware deep learning, self-supervised learning, and the creation of lightweight digital twins for smart cities, environmental monitoring, and immersive visualization applications.

Student roles:
The student will assist with the development and evaluation of AI methods for analyzing large-scale 3D point cloud datasets. Responsibilities may include data preprocessing, visualization, annotation refinement, implementation of machine learning algorithms, and experimental evaluation of 3D deep learning models.

The student will work with modern software tools for point cloud processing and contribute to benchmarking studies using real-world urban LiDAR data. Tasks may involve generating visualizations, analyzing model performance, preparing datasets for training and testing, and documenting experimental results.

Depending on the student's interests and progress, additional activities may include investigating geometric and topological features of 3D structures, developing interactive visualization tools, or exploring applications related to digital twins and smart-city technologies.

The student will participate in regular research meetings, present progress updates, and contribute to technical reports and research publications where appropriate. Throughout the project, the student will receive training in machine learning, 3D computer vision, scientific programming, and research methodologies, providing valuable experience for graduate studies or careers in AI and data science.

Skills required:
Students should have a background in Computer Science, Data Science, Mathematics, Engineering, or a related discipline. Experience with Python programming is required. Familiarity with machine learning, linear algebra, data structures, and basic computer vision concepts is desirable but not mandatory. Experience with PyTorch, TensorFlow, or scientific computing libraries (NumPy, SciPy, Open3D, or scikit-learn) would be beneficial. Strong problem-solving skills, curiosity, and a willingness to learn new technologies are highly valued.

Research area: I work in areas related to controls and automation, focusing on achieving autonomy under uncertainty. Current interests include adaptive control, nonlinear control, control & optimization, learning-based control, and robotics & autonomous systems.

Student roles:
Under the supervision and guidance of the faculty supervisor, the role of the student throughout the duration of the project includes, but may not be limited to,

- conducting a literature review of the state-of-the-art on applications of neural networks to feedback control and summarizing already existing methods,
- learning and developing knowledge about advanced control topics such as adaptive control and learning-based control,
- identifying limitations and/or opportunities for improvement in learning-based control techniques,
- setting up, as needed, the simulation environment for robotic simulations,
- develop simulation models (i.e. writing computer code) to replicate existing results and test performance,
- meeting regularly with the supervisor to update on the progress of the project,
- writing a summary of the project findings in a report, and
- potentially writing and submitting a research paper reporting new novel findings.

Skills required:
- A student involved in this project is expected to have a background in control theory/feedback control systems.

- It would be preferable for the student to have knowledge in nonlinear control, adaptive control, and/or optimal control, but not necessary.

- Background in machine learning, robotics and autonomous systems will be useful.

- Programming skills in Python and/or MATLAB are expected.

- Familiarity with working on simulation environments such as PyBullet, Gazebo, MoJoCo, or similar ones should be helpful.

- Proficiency and familiarity in programming ML code and relevant NN packages is a plus.

Research area: I specialize in performance-based safety assessment, vulnerability evaluation, and multi-hazard analysis of critical infrastructure. My research advances resilience to extreme events and climate change by integrating infrastructure equity, community resilience, and climate justice. I also use data science to bridge built, natural, and social systems, developing holistic approaches to infrastructure risk, adaptation, and environmental challenges.

Student roles:
For an undergraduate intern, this project offers an opportunity to work at the intersection of civil engineering, data analysis, and infrastructure management. Depending on the student’s interests, background, and profile, responsibilities may include organizing and analyzing municipal infrastructure datasets, exploring condition and time-series data, supporting the development of predictive and machine-learning-based models, and comparing snapshot-based and longitudinal approaches for assessing deterioration and residual useful life.

The student may also contribute to data visualization, interpretation of results, literature review, and the preparation of research summaries, figures, and practical tools that support municipal decision-making. Throughout the internship, I will take into account the student’s interests and strengths in assigning tasks, so the experience can be both meaningful and aligned with their learning objectives.

The student will work closely with the research team and will also interact with professionals from the Centre d’expertise et de recherche en infrastructures urbaines (CERIU), gaining exposure to real-world challenges in municipal infrastructure management. This role is especially well suited to students interested in sustainable infrastructure, water systems, smart cities, and the use of data analysis and predictive modeling in applied research with practical impact.

Skills required:
I am looking for students with an interest in civil infrastructure, data analysis, and predictive modeling. Experience with Python and/or MATLAB is important for working with data, developing models, and creating visualizations. A good foundation in probability and statistics is expected, and familiarity with machine learning, regression, time-series analysis, and uncertainty analysis would be an asset. Students should be curious, motivated, and comfortable working with data to support practical engineering decision-making.

Research area: I do research in software engineering. I work on various techniques that involve source code analysis, execution traces, code transformation, software dependencies, build systems, with applications to internet-based systems and generative art systems. I have worked in France, Sweden and Canada with graduate students from all five continents. More details about my contributions and our current research crew: https://softwarediversity.eu/

Student roles:
The student will perform the following tasks
- curate a dataset of software applications
- build a complete model of the software supply chain of these applications, across all layers from source to runtime
- study techniques to determine the necessary supply chain
- experiment specialization transformations
- document the process and the results in a scientific report

Skills required:
Code analysis, code transformation, data analysis, compilers, containers

Research area: In my research group, we are interested in serious games: games and gaming-related technologies for the purpose of training, learning, and education. We develop AI algorithms (including search and machine learning) to enable adaptive and personalized experiences, and for automated content generation. The concept of a "game" extends beyond entertainment, as modern game engines (such as Unreal and Unity) are widely used for media production, simulations, and digital twins. We integrate XR immersive technologies (including extended reality, virtual reality, eye-tracking technology) as part of the experience.

Student roles:
The project will require the student to read and understand the existing code of the game, and modify it to add additional functionality. Web-based deployment will be required. The student will be required to create mock data to test each functionality. The student is expected to attend weekly research meetings and potentially contributing to research papers.

Skills required:
The student should be familiar with web development technology, specifically HTML, JavaScript, CSS, Node.js, React, TypeScript, JSON, as well as Java Spring framework.

Research area: In our research, we develop interactive computing systems leveraging human spatial thinking and physical manipulation abilities. Our work is grounded in tangible, embedded, and embodied computing (TEI) research, with a focus on systems that move away from traditional screen-based interactions to physical and embodied interactions with objects and spaces around us. This approach leads to more intuitive and effective human-computer interaction. We work in interdisciplinary teams, applying computer vision, machine learning, and interaction design to develop and evaluate advanced interaction systems for different application areas, including Learning and Collaboration, Data Visualization, Biomedical Computing, and Digital Narratives.

Student roles:
The student's role in this project would involve working with our mixed reality research team, which includes faculty members, graduate students, and other undergraduate researchers. The primary focus will be on advancing our current tangible MR system’s capabilities and developing new features for the MR platform by building machine learning and/or computer vision pipelines, and also new MR interactions. Based on the student's background and interests, they may focus on one or more of the technical development, interaction design, or a combination of both.
Specific responsibilities may include:
• Conducting background research on tangible and embodied interaction
• Analyzing and designing new interaction scenarios for physical-digital object manipulation
• Implementing new prototypes and interfaces using Unity 3D, Unreal, and/or other relevant platforms as needed
• Developing and integrating Computer Vision-based pipelines for enabling new MR interactions
• Developing and integrating Machine learning pipelines for improving system capabilities
• Evaluating computing systems and pipelines for efficiency
• Assisting in user study design and execution
• Contributing to the documentation of the system

The internship would take place in Synaesthetic Media Lab (Synlab) at Toronto Metropolitan University (TMU). Synlab is an interdisciplinary research lab, where members come from diverse educational backgrounds, including computer science, digital media, human computer interaction, and biomedical engineering. Our project also has partnerships with teams at University of Toronto and University Health Network (UHN), providing students exposure to various research approaches and potential career paths in academic research and industry innovation. Students will have the opportunity to contribute to cutting-edge research in tangible and embodied interaction while developing practical skills in mixed reality development and interaction design.

Skills required:
This project is seeking students for supporting the technical implementation of new and advanced features for our MR platform. Students should have a background in at least one of the following areas: computer science/software engineering, machine learning, computer vision, 3D interactive system development with Unity or Unreal. Experience with 3D design and prototyping tools like Arduino or Fusion 360 would be an asset.
Students should be comfortable with iterative design processes and working in a research environment. They should demonstrate strong problem-solving abilities, and be open to critical thinking and design thinking approaches.

Research area: My research interests span various areas in systems software and distributed systems. I am interested in designing large-scale software systems, with a focus on improving the performance, scalability and reliability of these systems.

Student roles:
In this project, the student will start by running experiments and evaluating the performance of the Epic GPU database. Currently, Epic is a single-node database that runs on a single GPU. The student will work to enhance Epic so that it supports distributed transactions running on multiple GPUs.

Skills required:
- Strong expertise in C and C++
- Taken course in operating systems
- Basic knowledge of the internals of the Linux operating system
- Knowledge of scripting languages such as Python
- Worked with source code tools such as Git

- Preferably taken courses in databases and distributed systems
- Preferably done GPU programming
- Preferably worked on some open-source projects

Research area: My research is on functional programming and dependent types. Specifically, I'm interested in ways that we can use types to ensure that programs are free of bugs, and to use types to guide the development of programs in a structured, rigorous way. Such type systems are sometimes difficult to use, so my research aims to develop advancements which improve the usability of such tools.

Student roles:
The project consists of three main phases.

* Evaluation: at first, the student would familiarize themselves with the core concepts of higher-order unification and the decidable fragment of it, as well as existing implementations of it. As a starting point, they could then implement their own version of non-parallel implementation. As part of this phase, the student will identify key parts of the implementation which can be parallelized.

* Implementation: together, the student and supervisor will devise a strategy for implementing parallelism into the implementation of unification. The student will write the code which implements this, along with a suite of tests to ensure its correctness.

* Evaluation: finally, the student will record the performance of their parallel implementation, and evaluate the degree to which, if any, it is faster than the sequential version.

The student will have freedom to try different techniques and explore different avenues for parallelization. Discovering which techniques are infeasible is itself a valuable contribution.

Skills required:
The student should be familiar with functional programming, algebraic datatypes, and higher-order functions, as well as the basic concepts of programming language implementation (syntax trees, tree-walking interpreters, recursion, etc.).

Other knowledge areas that would be useful, but are not required, include:
* Proof assistants or dependent types
* GPU programming
* Threading and systems programming
* Distributed systems
* Abstract math (abstract algebra, category theory, discrete math, formal logic, etc.)

Research area: Machine learning, Game theory, rough sets, shadowed sets

Student roles:
Work with me to refine game-theoretic learning models.

Skills required:
Analyzing, Math proving, programming

Research area: Our research group is generally interested in designing and evaluating Serious Games - mobile games, social games, virtual reality, and web-based games. These are active Human-Computer Interaction (HCI) research areas. We are mostly interested in using these systems to solve problems that are of global importance especially in the area of health and wellness (e.g, promoting a healthy lifestyle, physical activity, healthy eating, smoking cessation) and safety and security (e.g., avoiding risky driving behaviour). Problem domains such as energy conservation and climate change are also of interest.

Student roles:
The student will join a team of faculty, postdoctoral researchers and graduate students working on various projects at the Human-Computer Interaction Lab. The student would work with graduate students on projects that involve developing and evaluating interactive systems. There is flexibility for the students to focus on a particular application; web application, mobile application, social computing, games, or some combinations. The student would have access to collections of computer science papers and other relevant literature and resources (software and hardware) needed for the project. We hope to publish at least a fully refereed paper at the end of the project and with additional work to be carried out remotely after the end of the Globalink visit we could possibly generate more publications.

Skills required:
The student needs to have some design skills, be interested in game development or learning programming. Previous experience with games design and development will be of advantage. Familiarity with or interest in pursuing graduate studies in one of the following areas would be highly desirable: human-computer interaction, game design, mobile computing, social computing, game design, visualization, data analytics, machine learning, text mining, information retrieval.

Research area: https://lassonde.yorku.ca/users/pvashisth areas of interest include Recommender Systems, Machine Learning, and Computer Science Education Research

Student roles:
Critical thinking, story telling, Design, development, documentation, research, research paper writing.

Skills required:
interest in educational technology, game-based learning, and computer science or mathematics education. A background in computer science, digital media, mathematics, software engineering, or a related area. basic knowledge of Discrete Mathematics. Since the project involves both prototype and full game development, experience with programming, web development, UI/UX design, or game development tools such as Figma, HTML/CSS/JavaScript, Unity, Godot, or similar platforms would be helpful. The ideal candidate should be creative, self-motivated, detail-oriented, and able to work independently under supervision. Good problem-solving, communication, and documentation skills , and survey, research paper writing skills are also important.

Research area: Our research focuses on using Gaussian Splatting for targeted, object-centric 3D reconstruction of diverse items, from large structures (e.g., towers) to small objects, concentrating efforts for high-fidelity models. This approach ensures detailed target capture and precise mesh extraction, delivering accurate models that streamline analysis/documentation and support diverse applications with robust mesh outputs.

Student roles:
1. Reconstruction Algorithm Development & Implementation
Assist in designing and implementing core components of the Gaussian Splatting pipeline, focusing on targeted, object-centric 3D reconstruction.
Contribute to developing methods for processing diverse input data representing various targets, from large-scale structures to smaller, individual items.
Collaborate on enhancing algorithms to achieve high-fidelity 3D model generation, ensuring detailed capture of designated objects' geometry and appearance.
2. Object-Centric Feature Enhancement & Mesh Generation
Focus on researching, developing, and refining techniques that enable robust object-centric focusing within the reconstruction framework.
Implement and optimize methods for extracting, processing, and validating high-quality geometric meshes from the Gaussian Splatting representations of target objects.
Investigate and experiment with different strategies to improve the detail, accuracy, and efficiency of reconstructing and representing these specific target objects/structures.
3. System Experimentation, Evaluation, & Documentation
Conduct comprehensive experiments using diverse datasets to evaluate the performance, reconstruction fidelity, and mesh quality of the developed system.
Analyze experimental results, identify current limitations and areas for improvement, and actively contribute to the iterative refinement of algorithms and the overall pipeline.
Assist in thorough documentation of the research process, system architecture, experimental setups, and key findings.

Skills required:
The ideal student should possess a strong foundation in computer science, with a particular emphasis on computer graphics. Proficiency in Python is essential; experience with C++ and familiarity with 3D graphics libraries (e.g., PyTorch3D, OpenGL, or similar) and visualization tools are highly recommended. Experience or strong interest in Neural Radiance Fields (NeRF), Gaussian Splatting, and deep learning frameworks (e.g., PyTorch, TensorFlow) is highly beneficial. A proactive and independent learner with excellent analytical, problem-solving, and communication abilities is essential. Prior exposure to 3D reconstruction pipelines or advanced 3D data processing techniques is advantageous but not mandatory.

Research area: My research focuses on machine learning for quantum networks. We work on software that routes entanglement through a quantum network which is the building block of a future quantum internet. Quantum networks distribute entanglement across long distances, but deciding how to route that entanglement is a hard and open problem. We use reinforcement learning and graph neural networks to design routers that can adapt to new and changing network topologies. Our aim is to build routing software that will support a future quantum internet.

Student roles:
The student is the primary researcher and builder on this project. They will turn the research question into working code and concrete results. This is a hands-on role. They will do daily technical work while meeting weekly with the project supervisor for direction and feedback. The project will run in five phases. In the first phase, the student will get up to speed by reading the core papers, and learning the open-source quantum-network simulator (such as SeQUeNCe from Argonne National Laboratory). They will define the metrics for the project. Second, they will build the experimental setup, by coding a family of training network topologies, a separate set of held-out test topologies, and the logging needed to evaluate performance. Third, they will reproduce two existing routing methods to serve as baselines. Four, they will design and implement a routing agent that uses a graph neural network to read the network topology and route entanglements on never-before-seen topologies. Five, they will run experiments to measure how well the agent transfers to unfamiliar networks and write up the results. The student will have to think critically, decide what to test, interpret why a method does or does not generalize, and judge whether a result is real or an artifact. They will be responsible for keeping a clean, documented and reproducible codebase, and for communicating their progress clearly in weekly meetings.

Skills required:
The core requirement is programming. The student must be able write non-trivial code in an existing simulator and be comfortable working across a multi-file codebase. They must have a basic grasp of the foundations in probability and linear algebra. Reinforcement learning and graph neural networks are both based on these. They need to read and understand several technical research papers and reproduce two published methods. It would be helpful to have familiarity with machine learning and reinforcement learning concepts, an exposure to a deep learning framework such as TensorFlow, and any prior exposure to graph neural networks or networking concepts.

Research area: Graphs are a fundamental data structure for modelling complex relationships in real-world systems such as social, biological, and knowledge networks. The k-core model identifies cohesive substructures and is widely used to uncover dense, functionally significant regions in graphs. While most existing work focuses on k-core in undirected graphs, little research has explored its application to other graph types. My research aims to extend the k-core concept to directed, weighted, attributed, temporal, multilayer, and hypergraphs, developing efficient algorithms to better capture structural properties and support scalable, real-time analysis of complex and dynamic networks.

Student roles:
The student’s main responsibilities will include:

1. Reading and Reviewing Literature: Reading and summarizing relevant research papers on k-core variants, cohesive subgraph models (e.g., k-truss, nucleus decomposition), and dynamic graph algorithms. This will help the student build a solid theoretical foundation and identify gaps in the literature.

2. Algorithm Design: Creating new algorithms or modifying existing ones to compute k-core structures in specialized graph settings, accounting for properties like edge direction, weight, and temporal changes.

3. Programming: Implementing the algorithms in C++ with an emphasis on performance, correctness, and modular design.

4. Experimental Evaluation: Designing and conducting experiments on both synthetic and real-world graph datasets to test the performance, scalability, stability, and correctness of the proposed methods.

5. Research Reporting and Writing: The student will contribute to writing a technical report or research paper summarizing the methodology, experimental results, conclusions, and references. The students will also draw related figures and tables.

Skills required:
The ideal student should have a strong background in computer science or a related field, with a specific interest in graph theory, graph databases, graph algorithms, and data structures. Proficiency in C++ programming is essential, including experience with object-oriented design, algorithm implementation, and efficient memory management.

Research area: Our lab investigates novel data visualization techniques for small mobile displays, with a focus on maximizing information density while preserving comprehension on smartwatch and smartphone screens. we investigate how space-filling principles can be generalized across a broader family of visualization types common in mobile health and fitness apps. We design and evaluate techniques that identify and exploit the unused visual space within common chart types to embed secondary data, studying the perceptual and cognitive limits of this approach on constrained displays.

Student roles:
In weeks 1–2, the intern will review literature on small-screen visualization, space-filling techniques, and the SF-LG paper and its related work, and conduct an audit of visualization types commonly used in mobile health apps. In weeks 3–5, the intern will systematically analyze available whitespace in candidate chart types, design embedding strategies for each, implement them as mobile prototypes, and refine through pilot walkthroughs. In weeks 6–7, the intern will finalize the study design — task battery, stimulus datasets, and baseline conditions. In weeks 8–10, the intern will recruit participants and run study sessions, collecting accuracy, response time, and confidence data across all visualization conditions. In weeks 11–12, the intern will synthesize findings into a design framework, write a final report, and prepare a presentation. The intern meets weekly with the supervisor and contributes to a paper draft.

Skills required:
Background in computer science, information visualization, or HCI. Proficiency in a data visualization library (D3.js, Vega-Lite, or Android Canvas or any other platform) is required. Experience with Android or WearOS mobile development is a strong asset. Familiarity with perceptual studies of data visualization is an asset. Interest in information visualization, wearable computing, and visual perception. Comfortable designing and running controlled user studies. Proficiency in Python or R for statistical analysis. Strong written communication skills.

Research area: I am a professor of Computer Science at Ontario Tech University, where I lead the Visual Computing Lab. Our group investigates deep learning methods for hyperspectral image analysis, compression, and understanding. A persistent bottleneck in hyperspectral research is the scarcity of labeled training data: hyperspectral cameras are expensive, and annotating hundreds of spectral bands per pixel is time-consuming. This project addresses that bottleneck directly using generative AI. More about our research can be found at http://www.vclab.ca.

Student roles:
* Generative Model Developer:

This student will design and train the conditional generative model for RGB-to-HSI translation. They will implement spectral consistency losses, evaluate synthesis quality on held-out scenes, and ablate architectural choices (diffusion vs. flow-matching vs. GAN-based backbones).

* Temporal Coherence and Downstream Evaluation Researcher:

This student will extend the per-frame model to produce temporally coherent hyperspectral video sequences and build an evaluation harness that measures the usefulness of synthetic HSI data for downstream tasks (unmixing accuracy, segmentation mIoU) when used as training augmentation.

Skills required:
* Strong proficiency in Python and PyTorch.
* Background in generative models: diffusion models, conditional GANs, or normalizing flows.
* Familiarity with image-to-image translation methods (pix2pix, CycleGAN, or similar).
* Exposure to video processing and temporal modeling is a plus.
* Interest in data-centric AI and synthetic data generation.

Research area: This research area focuses on Generative AI, LLM workflow development, knowledge graphs, construction project analytics, and resource planning under uncertainty. The project explores how AI systems can transform incomplete project information into structured schedules, task breakdowns, resource estimates, and decision-support outputs for large construction, infrastructure, mining, and industrial projects.

Student roles:
The student will support the development of a Generative AI research prototype for construction planning and resource analysis. Responsibilities may include collecting and cleaning project data, structuring schedule information, mapping project activities into standardized schemas, and preparing datasets for AI-based planning workflows.
The student may help design LLM workflows that generate task breakdowns, identify precedence relationships, estimate durations, and produce structured project outputs. This may involve prompt engineering, RAG/GraphRAG implementation, LangChain or LangGraph workflow development, structured JSON generation, and validation of AI-generated planning outputs.
The student may also contribute to machine-learning components for duration and resource estimation, uncertainty modeling, and comparison of alternative project scenarios. On the software-development side, the student may help build backend APIs, experiment pipelines, dashboards, and visualization tools for project schedules, resource-demand curves, uncertainty ranges, and planning constraints.
The student is expected to write clean and reproducible code, maintain technical documentation, evaluate system performance, analyze errors and limitations, prepare visualizations, and contribute to research outputs such as prototype demonstrations, technical reports, conference papers, and journal manuscripts.

Skills required:
The student should have strong skills in Python, applied AI development, and data processing. Experience with LLMs, Hugging Face, LangChain/LangGraph, RAG, GraphRAG, vector databases, knowledge graphs, APIs, Git, and dashboard development is highly desirable. Knowledge of project scheduling, construction management, optimization, Primavera P6/MS Project, or resource planning is an asset.

Research area: This research area is at the intersection of Generative AI, vision-language models, agentic AI systems, and construction safety intelligence. The project focuses on developing advanced AI workflows that combine fine-tuned multimodal models, retrieval-augmented generation, LangChain/LangGraph-based agentic systems, structured risk reasoning, and smart-glasses integration to support real-time hazard recognition and safety decision-making in construction environments.

Student roles:
The student will support the development and evaluation of advanced Generative AI components for the smart-glasses safety system. The role may include implementing fine-tuning workflows for LLMs or VLMs, preparing domain-specific datasets, designing instruction-tuning formats, and evaluating model behavior for construction hazard recognition and risk reasoning.
The student will also help develop agentic AI workflows using frameworks such as LangChain or LangGraph. These workflows may include specialized agents for hazard detection, unsafe condition/action classification, risk assessment, control recommendation, evidence checking, and final response generation. The student will support integration with RAG or GraphRAG systems so that model responses can be grounded in construction safety knowledge, standards, risk matrices, or project-specific documents.
On the software side, the student may help build backend APIs, experiment pipelines, logging systems, dashboards, and structured JSON output formats. They will also support evaluation of system performance using metrics such as hazard detection accuracy, risk classification quality, reasoning consistency, response latency, token cost, and suitability for smart-glasses deployment.
The student is expected to produce clean and reproducible code, maintain technical documentation, analyze failure cases, compare alternative model architectures, and contribute to research outputs such as prototype demonstrations, technical reports, conference papers, and journal manuscripts.

Skills required:
The student should have strong experience in Python and applied AI development. Experience with PyTorch, Hugging Face Transformers, LLM/VLM fine-tuning, PEFT, LoRA/QLoRA, LangChain, LangGraph, RAG, vector databases, FastAPI, Docker, Git, and model evaluation is highly desirable. Knowledge of computer vision, MLOps, cloud/HPC deployment, or construction safety is an asset.

Research area: My research examines how generative AI can support teaching and learning in higher education, with particular attention to self-regulated learning, equity, and governance. I study how students use AI tools during authentic academic tasks, how AI shapes planning, monitoring, and problem solving, and how institutions can design responsible AI-supported learning environments. This project focuses on programming education as a meaningful context for investigating continuity of access, learner support, and AI-enabled educational design.

Student roles:
The student will serve as a research intern and will not be expected to teach. Responsibilities may include reviewing literature on generative AI in education, helping refine research instruments and study procedures, organizing and cleaning data, assisting with qualitative and quantitative analysis, and contributing to manuscript drafting. Depending on background and interest, the student may also support the co-design of AI-supported learning tools, including features related to MetaMentorAI.

The role is best suited to a student who enjoys interdisciplinary research and is interested in how AI can be studied and designed for meaningful educational use. The intern will gain experience in research design, mixed-methods inquiry, scholarly writing, and collaborative work at the intersection of AI, learning, and higher education. The internship offers mentorship in academic research and opportunities to contribute to conference or manuscript preparation arising from the project.

Skills required:
Preferred background includes computer science, educational technology, learning sciences, data science, or a related field. The student should have strong academic reading and writing skills, an interest in generative AI and higher education, and some experience with research methods. Experience with programming, human-computer interaction, literature reviews, or educational data analysis is strongly preferred. Familiarity with qualitative or quantitative analysis and AI-supported learning design would be an asset.

Research area: My research advances realism-aware, low-latency generative video compression, combining theoretical models with deep generative learning to enable accurate and statistically realistic causal reconstruction for data-driven systems.

Student roles:
- literature review
- deep learning experiments
- writing a paper

Skills required:
HQP will receive hands-on training in developing and deploying large-scale generative models such as diffusion models and GANs under strict memory and latency constraints, using multi-GPU systems with large high-bandwidth memory (HBM) for long video-sequence modeling, realism-aware reconstruction, and stochastic reconstruction pipelines.

Research area: My research fields are mostly about artificial intelligence (AI), machine learning, deep learning, natural language processing, large language models, and their applications. My recent research interest is in developing advanced deep learning models for understanding the semantics in structured data such as sequential signals, natural language text, and image data, and providing more intelligent interactions and analysis to target information systems. As part of my long-term research goal, this research project studies the state-of-the-art deep generative learning algorithms and agentic AI for jointly understanding and generating natural language and images for supporting semantics-aware interactions and reasoning in healthcare and educational

Student roles:
* retrieve relevant medical text and image data from open domain and open research resources.
* apply open source natural language processing tools including large language model based tools to text processing and model training and testing.
* use open source image processing tools for image processing and recognition.
* design selected deep generative models and algorithms for analyzing healthcare text and image data
* perform experiments for evaluating the designed algorithms and tools.
* develop a prototype system for demonstrating the integrated tasks of the application.
* write progress and development reports, and research papers.

Skills required:
* Major in Computer Science.
* Excellent skills in developing Python with advanced data structures and algorithms.
* Knowledge in artificial intelligence such as machine learning, deep learning, large language models, natural language processing and/or computer vision.
* Strong interest in learning exploring new models and algorithms of machine learning.
* Familiar with Linux programming and development environment.

Research area: My research group lies in the intersection of computational chemistry and machine learning. One of the main research interests is the acceleration of computational chemistry simulations by using modern numerical ecosystems or the use and development of machine learning models. Finally, my research group is also interested in applying modern optimization algorithms like Bayesian Optimization and automatic differentiation to enhance existing computational chemistry models.

Student roles:
The student will contribute to the development and extension of our current methodologies for derandomized CST protocols for excited states by designing, training, and testing generative models aimed at reducing measurement overhead in quantum simulations. This role offers hands-on experience in quantum computing and machine learning. The proposed timeline for the student researcher is the following:
Month 1:
The student will begin by reviewing CST protocols, generative modeling techniques, and relevant quantum computing libraries. They will become familiar with the current code and run baseline simulations. In this phase, they will assist in constructing and evaluating reward functions to guide circuit selection with generative models for excited states.
Month 2:
The student will benchmark the variance reduction achieved on small molecules, comparing generative sampling to baseline CST and classical electronic structure results. The student will simultaneously add restrictions for circuit selection to conserve relevant symmetries like particle number and spin, documenting results.
Month 3:
In the final month, the student will apply the optimized CST protocol to simulate excited-state and potentially, spectroscopic properties of selected molecules. They will analyze the measurement cost reduction and assess accuracy relative to ground truth values. The student will prepare a short report or presentation summarizing their methodology, results, and proposed improvements.
Throughout the project, the student will work closely with the research team, gaining experience in quantum simulations and generative AI. During the placement, the student will actively participate in weekly group meetings.

Skills required:
Basic Linux and Python knowledge.
he student should have taken an undergraduate level quantum mechanics course.
Desirable familiarity with PyTorch/JAX and quantum computing libraries like Pennylane.
Some experience with generative models and graph neural networks is beneficial but not essential.

Research area: Philip Jackson, Ph.D., is Professor at the School of Psychology at Université Laval. He is a researcher the CERVO and Cirris centres in Quebec City, and co-lead of research at the Observatoire sur les impacts sociétaux de l’intelligence artificielle et du numérique (Obvia). His interdisciplinary research team studies different aspects of social cognition, notably human empathy in the context of human diversity, using a variety of cognitive neuroscience and affective computing methods.

Student roles:
The intern will be invited to play an active role in the development of the EmotionStudio platform, used to create experimental tasks in psychology and affective computing. The main objective of the internship will be to help make the platform available online, enabling remote use and facilitating data collection from a larger and more diverse population. In collaboration with the research team, the intern will first become familiar with the scientific and methodological objectives associated with EmotionStudio, then learn its current functionality in order to identify the requirements for its online deployment. The intern will then be able to develop a set of specifications outlining the main steps needed to achieve this goal. Based on these specifications, the intern will plan the different stages of the work and ensure regular monitoring of project progress. Depending on the identified needs, the intern may also be asked to develop new features, improve existing ones, or fix bugs to support the deployment and optimal use of the platform.

Additional aspects of the internship:
- Joining a team of undergraduate and graduate students, mainly from Psychology, but with some with other backgrounds such as Nursing, Neuroscience, and Computer Sciences
- Participating in bimonthly lab meetings to discuss ongoing projects within the team or methodological and theoretical aspects of scientific articles
- Collaborating with students to better understand their needs and translate them into clearly defined and feasible functional updates
- Depending on the student background, contributing to the design and implementation of experiments

Skills required:
Qualifications:
 Minimum of two years in a recognized program of Computer Sciences/Engineering, Cognitive sciences, Neuroscience, or anther relevant program
 Experience with 3D animation or human-computer interaction
 Proficiency with animation tools (e.g., Unreal) and relevant programming languages (e.g., JavaScript)

Core Skills and Abilities:
 Strong interest in affective computing research and scientific programming within an academic setting
 Autonomy, initiative, and ability to organize work within a software development project
 Ability to work in an interdisciplinary team
Additional assets:
 Interest in developing experimental tasks or analyzing data in psychology and cognitive neuroscience

Research area: My expertise is Bioinformatics and Machine Learning. My current research focuses on multi-omics analysis which provides novel information on the mechanisms of the biological process and cell states in disease development. We develop computational tools for complex and high-dimensional data including genome-wide population data, RNA-seq, and tandem mass spectra. I'm particularly interested in applying deep learning and AI to plants to advance digital agriculture.

Student roles:
The student will implement, test and document the main components of a modern genomic selection pipeline. Tasks include training and evaluating models on real breeding data and producing reproducible code and reports. Specific technical topics include:
• SNP matrix preprocessing and quality filtering, including marker filtering, minor allele frequency thresholds, and handling of missing data
• Genotype imputation workflows and evaluation of different imputation strategies
• Dimensionality reduction and feature representation approaches such as principal component analysis, sparse feature selection, and autoencoder-based embedding
• Implementation and comparison of statistical and machine learning models, for example genomic best linear unbiased prediction, Bayesian regression approaches, random forest, gradient boosting, and neural network-based models
• Development of cross-validation strategies that reflect breeding practice, for example leaving whole environments or breeding cycles out of the training set and forward prediction where models are trained on past years and tested on future years
• Regularization techniques and model selection procedures that reduce overfitting and improve model stability
• Multi-environment and reaction norm type models to account for genotype by environment interaction and to improve across-site generalization
• Model interpretability and stability analysis, including feature importance, variance decomposition, and sensitivity to training data

Skills required:
1. Strong foundation in quantitative genetics, plant breeding principles, and an understanding of genomic selection and DNA marker-based prediction methods
2. Proficiency in statistical modeling and machine learning, including experience with regularization methods, cross-validation strategies, and handling high-dimensional data (p>>n problems)
3. Computational skills including scripting in R or Python, with familiarity with genomic analysis packages (e.g., rrBLUP, BGLR, scikit-learn) and command-line tools for large-scale data processing
4. Understanding of population genetics concepts including population structure, relatedness, linkage disequilibrium, and genotype-by-environment interaction
5. Experience or strong interest in working with large genomic and phenotypic datasets, high-performance computing environments, and reproducible

Research area: This project develops a Geographic Tangible User Interface (GTUI) to enable collaborative exploration of migration patterns in support of the wider Complex Migration Flows and Multiple Drivers in Comparative Perspective (MEMO’s) project. The GTUI will employ multisource datasets across South/Southeast Asia, West Africa, and the Americas. Combining active tangibles with geospatial visualization, it translates complex flows from MEMO’s research into interactive tabletop and wall maps. Targeting a diverse range of migration researchers, the GTUI supports multi-scale analysis through hybrid interaction modes, fostering interdisciplinary dialogue between quantitative modelers and qualitative field researchers – aligning with MEMO’s focus on bridging methodological divides.

Student roles:
The student will:
(1) Develop software integration for infrared stylus pens and active tangibles using embedded systems (Arduino) and spatial interaction logic in C++/Python, while designing map-based interfaces with JavaScript/Leaflet for migration data visualization and geospatial model building.
(2) Develop software integration of active tangibles on interactive multi-surface displays including both tabletops and walls using Node.js and Socket.IO.
(3) Conduct iterative user studies employing qualitative methods (workshops, focus groups, contextual interviews, think-aloud protocols) and quantitative metrics (task completion rates, gesture accuracy) to evaluate GTUI usability in migration scenarios.
(4) Co-facilitate interdisciplinary collaboration by translating geospatial analysis (QGIS/PostGIS workflows) into tangible interaction patterns and co-designing tabletop experiences with urban planners and migration researchers, and:
(5) Document and disseminate findings through IEEE and TEI-style papers (LaTeX-formatted technical reports), comparing spatial interaction techniques across prototyping iterations.

Skills required:
(1) Programming proficiency in Python (for geospatial libraries like GeoPandas/ArcPy and web applications like Node.js/Socket.IO) and JavaScript (for web mapping frameworks such as Leaflet/OpenLayers), supplemented by familiarity with C++/Java for hardware integration in tangible interfaces.
(2) Familiarity with regression machine learning models such as XGBoost, Random Forest and Explainable AI methods such as LIME and SHAP.
(3) Data storytelling abilities demonstrated through projects combining map-based migration analysis with user-centered visualization techniques, and
(4) GIS expertise in spatial analysis, cartography, and data visualization using tools like ArcGIS, QGIS, or PostGIS, with experience handling migration datasets.

Research area: Machine learning is revolutionizing the way we interpret medical imagery. By leveraging modern algorithms to extract complex geometrical patterns, we empower clinical experts to detect anatomical anomalies with unprecedented precision. Our ultimate mission is to harness these computational advancements to save lives and significantly enhance patient outcomes through data-driven insights. Dates are flexible.

Student roles:
The student will advance state-of-the-art algorithms on image segmentation and data analysis, applied on cardiac or brain imaging.

To do so, work will be:
- Proposing and reading related literature,
- Designing, programming and validating algorithms,
- Preparing a research article.

The internship will follow these steps:
- Presentation at mid-internship and end-internship
- Week 1-2: literature review,
- Week 2-4: literature review and code design,
- Week 4-6: design of validation experiments,
- Week 6-12: validated code, draft of article if ready.

Communication will be based on:
- Weekly meetings within our group and one-on-one meeting,
- Weekly report on current status

Skills required:
The candidate should have strong computer programming skills, and adequate algorithmic and mathematical skills, all necessary to improve his creativity in designing new machine learning and computer vision algorithms.

Research area: Matt Brehmer (he/him) is a human-computer interaction (HCI) researcher focusing on data visualization: he designs, implements, and evaluates new ways to communicate and collaborate around data. He is a member of the HCI Lab and the Future Cities Institute, and he leads the ubietous information experiences research (ubix) group. The continued relevance of our research is rooted in the conviction that some people have an intrinsic curiosity with respect to data and want to share this curiosity with others through the creative medium of visualization.

Student roles:
The student will join Professor Matt Brehmer's "ubietous information experiences" research group (https://www.ubixgroup.ca/p/join), part of the University of Waterloo's School of Computer Science Human-Computer Interaction (HCI) lab (https://hci.cs.uwaterloo.ca/). As a member of the group, the student will occupy a seat in the HCI lab and participate in weekly research group meetings. The student will also meet individually with Prof. Brehmer, at least once a week. The student is expected to prepare for these individual meetings and maintain a shared set of meeting notes, summarizing recent progress, next steps, and open challenges.

As far as the project is concerned, the student will be expected to engage in several different activities throughout the course of the internship. This task switching will require flexibility and effective time management. Some of these activities involve interfacing with stakeholders such as educators and students, requiring effective speaking, listening, and note-taking abilities. Other activities will be technical, extending the recent work of other group members; these activities will require the reading and writing of technical documentation. The alternation between interface design, prototyping, and implementation activities will continue throughout the internship, with each activity producing artifacts and code maintained with version control systems. See the associated activities list for a sense of the expected frequencies of these activities.

The intended outcomes of this internship include: (1) at least one deployed instance of a gesture-aware augmented reality video application populated with content and interfaces relevant to an online STEM course or tutoring syllabus; and (2) draft content for a research paper manuscript describing the project that can be extended and submitted to an HCI research conference or journal, with the intern positioned as the lead author (…finalizing, submitting, revising, and presenting the paper upon its acceptance at a conference will fall outside of the time frame of the internship).

Skills required:
1. Proficiency with the web development technologies used in our current implementation (https://github.com/ubixgroup/Glass-Chirolytics), or closely related technologies. 2. A foundational understanding or user interface design paradigms and some familiarity with visual perception and gestural interaction. 3. A willingness to interview and observe educator and student stakeholders as part of the process of adapting existing STEM curricula to modules incorporating our approach. 4. Comfortable reading academic papers and technical documentation, along with a willingness to develop academic writing skills.

Research area: Graphs are a fundamental data structure for modeling complex relationships in real-world systems, including social networks, biological networks, and knowledge graphs. Designing efficient graph algorithms is a challenging and time-consuming process that requires algorithmic innovation, correctness verification, complexity analysis, architecture-aware optimization, implementation, and extensive testing. These challenges become even more significant for sophisticated graph analytics tasks such as cohesive subgraph discovery.

Student roles:
1. Literature Review and Analysis: Conducting a comprehensive review of research papers on k-core decomposition, cohesive subgraph discovery, Large Language Models (LLMs), and LLM-assisted algorithm design. The student will summarize key findings, identify research gaps, and develop a strong theoretical foundation for the project.

2. LLM-Assisted Algorithm Development: Designing and refining prompts for LLMs to support graph algorithm development. The student will interact with LLM APIs, evaluate generated outputs, and investigate how LLMs can assist with algorithm ideation, implementation, correctness verification, optimization, and testing.

3. Software Development and Implementation: Implementing graph algorithms and experimental frameworks in a suitable programming language. The student will integrate LLM-generated components when appropriate and develop tools to facilitate automated experimentation and evaluation.

4. Experimental Evaluation: Conducting systematic experiments to evaluate the effectiveness of different LLMs (e.g., GPT, Gemini, and Claude) in assisting graph algorithm development. The student will measure factors such as development time, implementation quality, correctness, performance, and productivity gains achieved through LLM assistance.

5. Research Reporting and Scientific Writing: Contributing to the writing of research manuscripts that document the methodology, experimental results, analysis, and conclusions. The student will also create figures, tables, and visualizations to effectively communicate the research findings.

Skills required:
The ideal student should have a strong background in computer science or a related field, with a specific interest in graph theory, graph databases, algorithms, and data structures. Proficiency in C++ programming is essential, including experience with object-oriented design, algorithm implementation, and efficient memory management.