Contacting Professors
Before completing a matching application, please consider contacting professors directly. This is their preferred method to connect with students, as it allows for a dialogue, so that they can better get to know you and align your skills with potential research projects. Here are some tips for finding a research project on your own:
- Look up professors on department webpages. Every department has a directory of researchers, either under "Department Members" or "Contact Us" tabs.
- Look at their webpages, and find professors conducting research in areas that you are interested in.
- When you find a professor that you would like to connect with, send them a professional email with the following information:
- Your name, program, and academic level.
- Ask them if they are looking for undergraduate research assistants at this time.
- Identify which parts of their research that you are interested in,Ìýand explain why.
- Provide a brief description of skills you have or courses that you've taken that align with this research, and would help you contribute to a research project.
- Consider attaching your CV and a copy of your transcript.
Projects and Application Form
If you are an undergraduate student interested in participating in a research internship with a professor listed below, please fill out a to be connected with a potential supervisor.
Once a supervisor has been secured, you mayÌýproceed to the next step in the application process, described by the corresponding department inÌýUndergraduate Student Research AwardsÌý²õ±ð³¦³Ù¾±´Ç²Ô.
Professor |
Department / School |
Project Title |
Project Description |
Must-have Skills and Courses |
| Michael Wallace | Statistics and Actuarial Sciences | Generative AI use in Statistical Methodology Literature Review and Code Generation |
This project will explore the use of generative AI (such as ChatGPT) in conducting literature reviews and coding tasks within statistical science. The following summary is deliberately vague about the specific methods under study, but note that they will be within the biostatistical sciences. The first objective of the project will be to assess the use of AI in emulating a literature review that has already been conducted by a postdoctoral scholar. This review aims to identify and summarize the extensions to an existing statistical method. You will use generative AI to attempt to emulate this review, noting any strengths and/or limitations of the approach. The postdoctoral scholar will be available to review your work and provide input and feedback. The second objective of the project will be to use generative AI to assist in extending an existing R package that implements the methods identified in the literature review conducted under Objective 1. The project will conclude with a written report summarizing the findings of both objectives. In particular, the report will discuss the suitability (or otherwise) of generative AI for such tasks, as well as any recommendations for future researchers interested in employing generative AI in such contexts. |
Mathematics and statistics background. Strong programming skills (project will be completed in R). Familiarity with generative AI/LLMs (an understanding of the underlying theory is a plus). |
| Mohamed Hibat-Allah | Applied Math | Language models for quantum many-body physics and combinatorial optimization | Theme 1: Using language models to study quantum systems (e.g., quantum spin systems, cold atoms, molecules, etc.) Theme 2: Building language model-based solvers of combinatorial optimization problems. |
Proficiency in Python coding and experience with one of the three machine learning libraries (TensorFlow, PyTorch, or Jax). Knowledge of quantum mechanics and/or statistical physics. |
| Computer Science | Programming languages: design, type systems, compilers, verification, probabilistic programming, security | I design and implement programming languages. I aim for language abstractions with rich expressive power, fast implementations, and strong guarantees. | Strong programming skills; strong mathematical reasoning skills | |
| Computer Science | Interacting with visual representations of time-oriented data in augmented reality | (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 directs the ubietous information experiences research group. This URA project will compare egocentric vs. exocentric / world-anchored positioning of time-oriented data in mobile augmented reality. | Candidates should have experience working with frameworks such as Apple's ARKit or Google's AR Core, prototyping and 3D modelling with tools like Reality Composer, Sketchup, Blender, or Unity, and implementing gesture- and / or proxemic-based interactions. Candidates should have completed and done well in CS 349 (User Interfaces). | |
| Computer Science | A queryable data lake for Open Government | This project seeks to allow users to ask natural language questions to request information over a naturally messy collection of data. This data is owned by multiple parties (e.g. they do not share schema and sometimes not even common collection practices). This data comes in various format (e.g. tabular, unstructured text, HTML pages, images, time series).ÌýÌýa benchmark for this problem setting using real, naturally disjoint, naturally heterogenous data. We would like to build three such data lakes to establish a benchmark so that the research community can actively develop insights and practical artifacts for this problem setting. This MURA project is on building a data lake usingÌýÌýdata provided by the Canadian Government. |
Required: Software engineering, web crawling Optional but preferred courses: databases, algorithms, operating systems Optional but preferred experience in: natural language processing,Ìýmachine learning |
|
| and | Computer Science | Fast Matrix Multiplication in Machine Learning | Matrix multiplication is a cornerstone of modern machine learning (ML), pivotal in transformer and large language models (LLMs). This project seeks to investigate novel methods to accelerate matrix multiplication, drawing from our recent state-of-the-art research. By optimizing this fundamental operation, we aim to enhance the efficiency and performance of ML systems. | You will engage with cutting-edge ML models and real-world data workloads, gaining invaluable experience in both academic research and practical applications. You must have a strong software engineering background, including experience with C++ and Python. Experience in ML model training and inference, as well as CUDA programming, is preferred. |
| Roberto Guglielmi | Applied Math | Control systems / optimization / PDEs | Topics might focus on more analytical or computational aspects of the problem. Projects can be found on Dr. Guglielmi's lab page. | AMATH 250/251 and beyond |
| School of Computer Science |
1. Computation over Encrypted Data 2. Machine Learning Security and Privacy |
1. There exist cryptographic techniques such as homomorphic encryption and secure multi-party computation but they require careful application in order to be practically efficient. We work on several projects that apply these techniques, such that the computational overhead is reduced. 2. More and more applications use machine learning to derive insights from large data collections. However, this process is susceptible to several security and privacy threats, such as poisoning or evasion attacks. We work on several projects that help ensure that such threats are contained. |
Not Specified | |
| School of Computer Science |
Natural Language Processing / Information Retrieval |
Java |
||
| School of Computer Science | Model and analyze software-intensive systems to improve their quality and safety | Areas of research: software engineering, model-driven engineering (MDE), modelling and analysis, formal methods, system safety, requirements specification and analysis. | Interest in logic and software engineering; likely having taken SE212 or CS245 | |
| School of Computer Science |
Natural Language Processing, Computational Linguistics, Machine Learning |
Current À¶Ý®ÊÓƵ students and prospective visiting students: please complete a and submit the application following the instructions. |
Please complete a | |
| Applied Mathematics | Scientific computing, machine learning | Scientific computing, machine learning | An intro into PDEs and numerics |
Additional projects may be available through the following departments:
Combinatorics and Optimization
If you see a project that you're interested in, please complete a .
Submission Deadlines for Fall 2025:Ìý
- Co-op: April 30
- Regular Stream: July 14