Events by month

Please note: This PhD seminar will take place online.

Raniah Alghamdi, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Richard Trefler

Please note: This PhD seminar will take place online.

Valerie Platsko, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Kate Larson

Please note: This master’s thesis presentation will take place in DC 2310 and online.

Sonja Linghui Shan, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jeffrey Shallit

Please note: This master’s thesis presentation will take place in DC 3317.

Adrian Cruzat La Rosa, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Diogo Barradas

Please note: This PhD defence will take place in DC 3317 and online.

Chendi Ni, PhD candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Yuying Li, Peter Forsyth

Please note: This master’s research paper presentation will take place online.

Muhammad Arsalan Khan, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Shane McIntosh

Please note: This PhD seminar will take place online.

Ben Armstrong, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Kate Larson

We present our work using machine learning models to approximate social choice functions, a.k.a. methods of voting. Voting rules are functions that are given voter preferences and produce a winning candidate.

Please note: This master’s thesis presentation will take place in DC 2310.

Renee Leung, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jesse Hoey

Please note: This master’s thesis presentation will take place in DC 2314, not DC 3317. 

Lucas Fenaux, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Florian Kerschbaum

Please note: This seminar will take place in DC 3317 and online.

Maxime Roland René Flin
Reykjavik University, Iceland

Please note: This PhD seminar will take place in DC 1304.

Xueguang Ma, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jimmy Lin

Neural retrieval systems have proven effective across a range of tasks and languages. However, creating fully zero-shot neural retrieval pipeline remains a challenge when relevance labels are not available.

Please note: This master’s thesis presentation will take place online.

Benyamin Jamialahmadi, Master’s candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Ali Ghodsi, Mohammad Kohandel

Please note: This seminar will take place in DC 1304 and online.

Chris Trevisan, Undergraduate student
David R. Cheriton School of Computer Science

Please note: This master’s thesis presentation will take place online.

Fadhil Abubaker, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Khuzaima Daudjee

Please note: This master’s thesis presentation will take place in DC 2310.

Prabhjot Singh, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Diogo Barradas

Although encrypted channels, like those provided by anonymity networks such as Tor, have been put into effect, network adversaries have proven their capability to undermine users’ browsing privacy through website fingerprinting attacks.

Please note: This PhD seminar will take place online.

Shaokai Wang, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Bin Ma

Please note: This seminar will take place in DC 1304.

Ibrahim Numanagić
Canada Research Chair in Data Science and Computational Biology
Assistant Professor, Department of Computer Science
University of Victoria

The scale of biological data has increased exponentially over the last two decades. As Moore’s Law continues to slow, scientists can no longer rely on hardware to catch up with the ever-increasing size of biological data and need both scalable algorithms and effective programming environments to implement and test them.

Please note: This PhD seminar will take place in DC 3317 and online.

Robert Wang, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Lap Chi Lau

Please note: This PhD seminar will take place in DC 1304.

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).

Please note: This PhD seminar will take place in DC 2310 and online.

Nils Lukas, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Florian Kerschbaum

Please note: This master’s thesis presentation will take place online.

Seba Khaleel, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Samer Al-Kiswany

Please note: This PhD seminar will take place online.

Ryan Hancock, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Ali José Mashtizadeh

Please note: This PhD defence will take place in DC 3317 and online.

Nils Lukas, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Florian Kerschbaum

Please note: This seminar will take place in DC 1304.

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).

Please note: This seminar will take place in DC 1304.

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.