Faculty member's name |
Department / School |
Project title |
Project description |
Must have skills/courses by a candidate to conduct the project |
| Anamaria Crisan | School of Computer Science | Building out an Experimental Infrastructure for Human-AI Collaboration | This URA will help to develop a computational infrastructure for prompting, retrieving, and storing, responses from large language models (LLMs) and humans. Their work will support the development of a broad experimental infrastructure for examining human-AI collaboration across a variety of application contexts. | An ideal candidate should be a capable full stack developer, versed in front-end (e.g., Java Script, React, Svelte, Vega-lite, etc.), back-end (PostGreSQL, Duckdb, Docker, etc.), and data analysis (Python), languages and frameworks |
| Roberto Guglielmi | Applied Math | Control systems / optimization / PDEs | Topics might focus on more analytical or computational aspects of the problem. Please have a look at current research projects at the page. | AMATH 250/251 and beyond |
| Shlomi Steinberg | School of Computer Science | Specific Research Areas | Research areas of interest: Rendering, wave propagation, appearance reproduction | Computer Science Background |
| Ricardo Fukasawa | Combinatorics & Optimization | Integer Programming, Operations Research, Optimization | Researching large-scale optimization methods | Courses CO250 or CO255 should be completed and coding experience |
| 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 |
| Yizhou Zhang | School of Computer Science |
Programming languages: design, compilers, verification, probabilistic Programming
|
Yizhou's projects 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 |
| Lilia Krivodonova | Applied Mathematics | Scientific computing, machine learning | Scientific computing, machine learning | An intro into pdes and numerics |
| Henry Shum | Applied Math | Microhydrodynamics | Some potential topics include modelling the fluid flow generated by dissolving liquid crystal droplets, studying the hydrodynamic controllability of particles in a suspension, and inferring patterns of motor activity in microorganism flagella from videos. More details here. | Proficiency with programming (e.g., Python, MATLAB) is required. Courses in physics, differential equations, and computational methods are beneficial. |