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.
Students will participate in a four-month, full time, research-based co-op 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 available 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.
Students will receive a minimum of $12,000/term:
- The Cheriton School of Computer Science will contribute $7,500/student; and
- NSERC USRA + the faculty supervisor is to top up the difference
Our hope is that URF positions can be in-person for Summer 2022, but this will be dependent on COVID pandemic-related government and university restrictions and safety policies.
- 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, with a strong interest in research and/or graduate studies.
A student can only do one URF/Co-op per term, and must be on a co-op term or otherwise not taking courses. Note that International Students need a SIN# and work permit to work in Canada. During the term, it is expected that students will be working remotely from a location within Canada.
If you are eligible, prepare a single PDF file (titled YourFirstName_YourLastName.pdf), which must contain the following information:
- An up-to-date CV/Resume;
- A recent transcript;
- A one-page personal/research statement, stating
- what your research interests are,
- what your future plans are and why you want to pursue the URF,
- indicate which research projects (in order of preference) you are interested in working on. You can either name a faculty supervisor in the “Potential Supervisors” list below, or name a faculty member that you have an existing connection with.
- A letter of recommendation from a professor at your university (optional, but encouraged).
Please note that incomplete applications (i.e., missing any of the items above) will not be considered.
- If you are a co-op student, apply to our job posting through your university’s co-op system and submit your PDF file on the co-op site. If the system does not allow attachments, you can submit your PDF file to the URF application Vault folder.
- If you are not a co-op student, upload your PDF file to the URF application Vault folder.
|Spring 2022||January 25, 2022 (9:00 AM)|
Communicating private computation
Supervisor: Florian Kerschbaum
The development of private computation techniques increases opportunities for two or more companies to use their data collectively for analysis without disclosing private user data to one another. However, the use of private computation does not absolve companies from their responsibility to disclose their data practices to users. We aim to ensure users, who are the data subject and have provided their data to a company, can give informed consent. We intend to study the expectations users have as data subjects for companies that include their data in private computation. Our results could guide law and policy surrounding how companies communicate to users about private computation as well as provide researchers with a better understanding of users’ expectations for privacy in this setting.
Flight planning by machine learning
Supervisor: Hongyang Zhang
The project aims to simulate daily flows by deploying the first algorithm to an individual flight’s user preferred route. This is a planning exercise conducted on behalf of each airline/each flight, usually 3 hours prior to takeoff. To this end, planned upper meteorological data (licensed by Transport Canada) will be used. This data covers the entire globe and over 15 altitudes used by commercial aircraft, uses 36-hour ‘look ahead’ forecasts, and is updated 4 times daily. The objective is to design a reliable machine learning algorithm that analyzes the entire planned traffic sample flying each day across the North Atlantic (NAT). Tasks include: 1) Revising and re-assembling current 4D shortest path algorithm; 2) Parallelizing optimization runs to accommodate simultaneous flight computations; 3) Investigate the robustness, security, and reliability of the algorithm; 4) Investigate machine learning using model Planned versus Flown data sets. An ideal URF should have basic knowledge on machine learning and algorithms (e.g., the shortest path algorithm) and strong coding ability. Besides money support, successful URFs will get my recommendation letters or the opportunities to join my lab as graduate students.
Advanced hockey analytics
Supevisor: Tim Brecht
The NHL collects puck and player tracking data during hockey games. This data provides significant opportunities for new advanced hockey analytics and user engagement. The goal of this project is to develop analytics that can be used by players, coaches, general managers, broadcast analysts, fans and gaming/betting sites as well as in the development of advanced models of players and teams. (preliminary research)
Operating system kernels - Theory vs. Practice
Supervisor: Martin Karsten
Operating system kernels are fairly big and very complicated software entities that address complex resource management challenges and typically support a massive set of hardware devices. Meaningful research into the structure and performance of operating systems is hampered by a significant barrier to entry: A research operating system must support a reasonable set of modern hardware devices to obtain useful performance measurements beyond simplistic benchmark tests. The overall goal of this project is lowering that barrier to entry by building a simple kernel nucleus and combining it with 3rd-party open-source software to support a large variety of device drivers. The critical next step is hollowing out an existing open-source operating system kernel and making the hardware support components independent of the core generic resource management services. This will result in a novel open-source research platform that enables subsequent studies on structural and algorithmic innovations for operating system kernels.
Computational statistics in computer graphics
Supervisor: Toshiya Hachisuka
Statistical approaches have been used in computer graphics for many different problems. In particular, in light transport simulation for photorealistic image synthesis, stochastic estimation using Monte Carlo methods is the most popular and efficient approach. This project aims to investigate mathematically and numerically the effectiveness of specific statistical methods for problems in computer graphics and to discover more efficient computational methods. The student is expected to study existing statistical methods such as Monte Carlo methods, experiment with them by coding a test program in computer graphics and work on its mathematical formulation to understand why and how well it works on a problem in computer graphics.
Design multimodal accessible neurofeedback
Supervisor: Jian Zhao
Children with autism spectrum disorder (ASD) face many difficulties in daily circumstances, due to their impaired social and communicative functions as well as restricted interests in people and activities. Emerging studies have shown that neurofeedback training (NFT) games are an effective and playful intervention to enhance social and attentional capabilities in children with ASD, leading to improved behavior indicators. While existing studies have examined the effects of NFT games as interventions for ASD, current NFT games still have many limitations. This project aims to investigate novel interaction techniques (e.g., augmented reality, mobile sensing) for enhancing traditional NFT games with a broader range of other interventions. The outcomes of the project will lead to more engaging, accessible, and personalized NFT-based games for the large population of autistic children. An ideal candidate should be proficient or knowledgeable in game and graphics design, mobile platform programming (e.g., iOS), and basic machine learning.
Accessing simulation of liquids using dynamic triangle remeshing
Supervisor: Christopher Batty
Accurate computer simulations of the physics of liquids are important for a wide range of disciplines and applications: lava flows and splashing waves in visual effects-driven films; biological processes in the body such as digestion and blood flow; atomization processes in which a high speed jet of liquid disintegrates into a dispersed spray; and many more. A very effective way to represent the surface geometry of a liquid is as a deforming, wireframe-like mesh of connected triangles. However, while this method produces extremely accurate results, it remains quite slow. In this project, we will investigate strategies to significantly accelerate such methods, such as improved collision detection algorithms, more memory-efficient data structures, and multicore or GPU parallelism.