WebNotice

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 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 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 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 online.

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

Supervisor: Professor Samer Al-Kiswany

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 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 and online.

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

Supervisor: Professor Jeffrey Shallit