Chris Trevisan, Undergraduate student
David R. Cheriton School of Computer Science
Shaokai Wang, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Bin Ma
Ibrahim Numanagić
Canada Research Chair in Data Science and Computational Biology
Assistant Professor, Department of Computer Science
University of Victoria
Robert Wang, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Lap Chi Lau
Sheng-Chieh (Jack) Lin, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Jimmy Lin
Contrastive learning is a commonly used technique to train an effective neural retrieval model; however, it requires much computation resources (i.e., multiple GPUs or TPUs).
Nils Lukas, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Florian Kerschbaum
Ryan Hancock, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ali José Mashtizadeh
Shiori Sagawa, PhD candidate
Department of Computer Science, Stanford University
Machine learning systems are powerful, but they can fail due to distribution shifts: mismatches in the data distribution between training and deployment. Distribution shifts are ubiquitous and have real-world consequences: models can fail on subpopulations (e.g., demographic groups) and on new domains unseen during training (e.g., new hospitals).
Silvia Sellán, PhD candidate
Department of Computer Science, University of Toronto
Computer Graphics research has long been dominated by the interests of large film, television and social media companies, forcing other, more safety-critical applications (e.g., medicine, engineering, security) to repurpose Graphics algorithms originally designed for entertainment.
Yan Shvartzshnaider, Assistant Professor
Department of Electrical Engineering and Computer Science
Lassonde School of Engineering, York University