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, and 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 matching application to be connected with a potential supervisor.
Once a supervisor has been secured, you may proceed to the next step in the application process, described by the corresponding department in Undergraduate Student Research Awards section.
|
Professor |
Department / School |
Project Title |
Project Description |
Must-have Skills and Courses |
| Kenneth Zhou | Statistics and Actuarial Sciences | Machine Learning with Telematics and Longevity Data | The project explores how everyday mobility and activity patterns are related to regional mortality and longevity. We will use telematics data with geographic information to study the relationship between people’s daily activities and mortality experiences. The student will help clean and merge datasets, construct activity features, and create visualizations to understand these patterns. We will then build and evaluate machine learning models to assess how well these features explain or predict regional mortality and longevity. | Proficiency in Python and R is required; Solid knowledge in probability and statistics is required; Experience with GIS, telematics and mortality data is preferred. |
| Sujaya Maiyya | Computer Science | Designing Transactional and Private Vector Database | With the advent of large AI and language models, managing data in an embedding or a vectorized form is becoming increasingly common. Vector databases are customized DBMSs that effectively store and search embeddings. However, an embedding can reveal much about the underlying data, raising privacy concerns. In this line of research, we are developing data systems that enable securely storing and searching on data embeddings. On another line of research, we are interested in exploring how to handle read and write requests to vector databases (without focusing on privacy). Concepts from databases and distributed systems will be relevant for this project. |
Courses on databases and distributed systems would be helpful. For the privacy project, courses on introduction to cryptography would also be helpful. |
| Sujaya Maiyya & Matt Brehmer | Computer Science | Communicating privacy concerns in genetic data sharing | Rapid advances in DNA sequencing enable personalized therapeutics tailored to an individual’s genetic profile. Achieving this requires collecting and analyzing sensitive genomic and medical history data from thousands of individuals. However, storing and sharing such data raises significant privacy concerns. This project aims to clearly and persuasively communicate these privacy concerns via data visualization and “explorable explanation” interfaces to various stakeholders, including patients, policymakers, and medical researchers, with the goal of promoting responsible data sharing. | Interest and passing knowledge in privacy is preferred; experience / coursework in interactive user interfaces and human-computer interaction is also preferred. |
| Raouf Boutaba | Computer Science | Maximizing Capital Efficiency in Solana DeFi via Real-Time Analytics and Intelligent Arbitrage Mechanisms | We are building arbitrage for Solana Solana, Sui and Base blockchains. This project involves real-time arbitrage opportunity discovery and optimized arbitrage algorithm design. The project will also analyze historical arbitrage transactions to discover the relation between arbitrage opportunity, potential profits and trading volume and price volatility. Note the project mainly considers the arbitrage opportunities between DEXs, not between CEX and DEX. | * The student must have strong programming skills (Preferably Rust, Typescript) * Knowledge about smart contract and automated market maker is helpful * Knowledge about data processing and data analytics is helpful (note: data size is billions of rows. but not requiring big data processing (such as Spark HDFS). |
| Mario Ghossoub | Statistics and Actuarial Sciences | A Computational Approach to Pareto Optimality in Risk Sharing Markets | Assist with implementing some theoretical algorithmic results about Pareto-efficient risk exchange. | Good coding skills, ability to read and understand research papers, background in real analysis and measure theory. |
| Yuntian Deng | Computer Science | Extending NeuralOS for Real-Time Reasoning with Internet-Connected Inputs | NeuralOS is our recent project on simulating an operating system using a neural diffusion model, where each frame is directly predicted by the model conditioned on user inputs (mouse movements and keyboard inputs). I propose extending it to handle real-time internet communication, enabling data exchange with the external work. For my details please refer to my homepage. | Proficiency in PyTorch. Prior experience with training diffusion models is preferred. |
| Steve Drekic | Statistics and Actuarial Sciences | Queueing Analysis of a Multi-server System with Batch Arrivals | This research will focus on a particular class of queueing systems: multi-server queues with batch arrivals. Previous work I have involved myself with has simply considered the assumption of exponentially distributed service times, which makes the mathematical analysis much more tractable due to the existence of the memoryless property. This project will focus on how this assumption can be relaxed to handle a more general class of service time distributions. | Familiarity with stochastic processes (discrete-time and continuous-time Markov chains mostly) and general probability theory (random variables and their distributions), along with an ability to code using a computer programming platform/package. |
| Victor Zhong | Computer Science | Open call for undergraduate researchers for research on multimodal, language‑driven agents at the R2L Lab | Our lab develops multimodal, language‑driven agents that can perceive visual interfaces, generate code, and interact with real‑world computer environments, while creating open benchmarks and feedback‑based learning methods to evaluate and improve their performance. We also build enabling technologies—such as open data frameworks, reasoning‑rich models, and safety‑focused monitor systems—to advance safe, general‑purpose agents that can reliably assist users across diverse tasks. See more at r2llab.com/openings. You must be self-motivated and capable of leading your own project. | Experience with training/using machine learning models. Strong programming skills. Experience in leading projects. |
| 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. |
| Yizhou Zhang | 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 |
| Xiao Hu and Victor Zhong | 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 |
| Florian Kerschbaum | 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 |
| Jimmy Lin | School of Computer Science |
Natural Language Processing / Information Retrieval |
Java |
|
| Nancy Day | 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 |
| Freda Shi | School of Computer Science |
Natural Language Processing, Computational Linguistics, Machine Learning |
Current Waterloo students and prospective visiting students: please complete a practice task and submit the application following the instructions. |
Please complete a practice task |
| Lilia Krivodonova | 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 matching application.
Submission Deadlines for Winter 2025:
- Co-op: September 12
- Regular Stream: November 7