The Cheriton School of Computer Science is looking for exceptional students currently enrolled in a Computer Science program or related areas who have a keen interest in research and in pursuing graduate studies. This award is open to students at any university.
Students will participate in a four-month, full time, research term to work directly with a faculty supervisor in a particular research area (e.g., Computer Security, AI, Human-Computer Interaction, Theoretical Computer Science, etc.).
- Learn the research methodology related to a particular field of interest (see projects below)
- Review existing literature and develop new research questions.
- Work with a supervisor and a team of graduate students to address the research questions, which may involve developing systems, creating algorithms, solving mathematical problems, designing experiments, etc.
- Work on publications to disseminate research findings to relevant academic conferences.
Meet Dmitry
Dmitry is a URF award recipient and spent the Fall 2020 term under the supervision of Professor Joanne Atlee and Michael Godfrey on the Rex project. The key in Rex lies in minimizing the information extracted from source code, thereby allowing for the analysis of very large systems. The analyses themselves involve discovering paths within the “fact based” graph extracted by Rex. Dmitry’s role revolved around improving the accuracy of these analyses by incorporating control-flow information into the extracted facts. Dmitry shares the highlight of his experience working on the Rex project and how it compared to past co-op positions.
“I think my favourite part about the internship was the amount I learned. Reflecting back to my previous co-ops, I think this was probably the most independence and ownership I was ever given on any project. As a result, I had to very quickly and very thoroughly learn my problem’s domain such that I could make progress. I learned a lot about the compiler tools involved in static analysis, such as ASTs and CFGs, and I came to realize that I really enjoy static analysis work. Apart from interesting technical knowledge, I also learned a great deal about abstract problem solving, since that was a huge component of my daily routine. I learned that open-ended problem solving is something that I find very fulfilling, and the new skills that I gained from it will definitely help me in future work.” Dmitry Koberts
Application Deadline
| URF Term | Application Deadline |
|---|---|
| Winter 2025 | September 16, 2024 |
| Spring 2025 | TBD (mid January) |
| Fall 2025 | TBD (mid May) |
Compensation
URF recipients will receive a minimum of $12,000/term:
- $7,500 contributed by the Cheriton School of Computer Science
- Remaining provided by faculty supervisor (and/or NSERC USRA, Faculty of Mathematics MURA)
Eligibility
- Students who are currently in 3rd or 4th year are eligible to apply; exceptional students from earlier terms will also be considered.
- A cumulative average of at least 80% is required.
- Preference is given to students enrolled in the Computer Science major or related programs (i.e. any program that would prepare a student for Computer Science graduate studies).
- The faculty supervisor must have an appointment or cross-appointment in the School of Computer Science
A student can only do one URF per term, and must be either on a co-op term or not taking courses. Note that International Students need a SIN# and work permit to work in Canada. URF recipients normally work on campus at the University of Waterloo, but due to pandemic restrictions, some or all positions may be remote. If remote, it is expected that students will be working from a location within Canada.
Open and Sponsored Applications
There are two types of URF applications:
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A sponsored application means you and a faculty member already have a connection, you've already discussed a specific project to work on, and they're providing a reference letter that states that they'll hire you as a research assistant if you're awarded a URF.
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An open application means you want to be matched with a faculty supervisor. Potential supervisors list projects or research areas near the bottom of this page, and you will indicate your interest in specific projects in your application.
How to apply
The application has two parts: an online information form and supporting materials submitted to a secure file server.
Information Form
The information form gathers basic information and provides some additional checks that your application is submitted correctly. It should be completed when you’re ready to submit the single PDF with your cover letter, resume, and transcript (see next part).
[ Information Form ]
Supporting Materials
1. A single PDF containing a cover letter, resume, and transcript. Your PDF file must have the filename “YourFirstName_YourLastName.pdf” and it must contain these items in the following order:
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Your cover letter should be 1 to 2 pages with: your research interests; why you want to pursue a URF; what your future plans are for graduate school; why you're interested in working on the specific project(s) you selected or the project you're already sponsored for.
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Your resume should be up-to-date and highlight relevant skills and experience.
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Your transcript(s) should be current and list all of your undergraduate courses and grades.
2. A letter of recommendation from a professor (or equivalent person with authority). Each letter must be a PDF file with the filename “YourFirstName_YourLastName_ReferenceLastName.pdf”.
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For open applications, a letter of recommendation from a professor is optional (but encouraged).
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For sponsored applications, a letter of recommendation from the sponsoring faculty member is required.
All supporting materials must be submitted to this secure file server:
[ https://vault.cs.uwaterloo.ca/s/g8R9mtt5GEE6EgQ ]
Please note that incomplete applications (i.e., missing any of the required items above) will not be considered.
Open Projects and Faculty for Winter 2025
Specific Projects:
Ana Crisan
Personalized Explanations of Data Visualization Dashboards
Data visualizations are a common tool used to explain complex information to the general public. For example, during the COVID-19 pandemic, visualization dashboards were widely used to help inform the public of the severity and trajectory of the pandemic. This research will explore whether we can generate personalized explanations of data visualizations in accordance to an individual’s numeracy and graphicacy skills. We will develop example explanations and test them with people. Finally, we will explore to what extent and in what ways we can generate appropriate explanations on the fly for a variety of visualizations. Our research will contribute to a better understanding of how data visualization can be made more accessible to a public with varied abilities to understand the information they are being presented. It can inform better communication strategies, especially in complex and critical public crises.
Shane McIntosh
Detecting and Optimizing Smells in GitHub Actions
We aim to develop a robust tool that detects and resolves inefficiencies, or "smells," in GitHub Actions—GitHub's Continuous Integration (CI) offering. These inefficiencies can lead to substantial waste of CI resources, including prolonged build times and excess resource consumption. By focusing specifically on GitHub Actions, our project will quantify the extent of resource waste due to these inefficiencies and provide a tailored solution to optimize them. Similar tools exist for other CI platforms like Travis CI, so our approach may adapt and refine their underlying approaches to align with the specifics of GitHub Actions. Additionally, our tool will rank smells according to the amount of resource waste they produce, ensuring developers can prioritize their CI optimization efforts effectively.
Xi He
Randomization Intervals for Differentially Private Median Queries
Differential Privacy (DP) provides a strong privacy guarantee in data analysis by adding calibrated noise to query results, but it can be challenging to apply and interpret. To address this, recent research has focused on developing usable DP systems that abstract away the complexities of DP mechanisms. One component of such systems is a tool for analysts to interpret how DP affects query utility. Randomization intervals, which provide upper and lower bounds on the true answer, offer an interpretable view of the noise added by DP. Existing work has developed a method for computing randomization intervals for elementary queries but lacks a solution for more general queries like the median. This project focuses on developing effective methods for computing randomization intervals with DP guarantees for more general mechanisms.
Open Projects:
Matt Brehmer
I am a human-computer interaction (HCI) and data visualization researcher interested in new ways to communicate and collaborate around data: including data storytelling, collaborative spatial computing / augmented reality (AR) experiences around information, and immersive data analogies.
Yizhou Zhang
I design and implement programming languages. I aim for language abstractions with rich expressive power, strong guarantees, efficient implementations, and diverse applications. I invite broad-minded, technically strong students to work with me on fun ideas about programming languages.