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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

Friday, January 12, 2024 12:00 pm - 1:00 pm EST (GMT -05:00)

PhD Seminar • Artificial Intelligence • Learning Voting Rules Using Neural Networks

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 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.