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Tuesday, August 2, 2022 10:00 am - 11:00 am EDT (GMT -04:00)

Master’s Thesis Presentation • Machine Learning • Learn Privacy-friendly Global Gaussian Processes in Federated Learning

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

Haolin Yu, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Pascal Poupart

Tuesday, August 2, 2022 1:00 pm - 4:00 pm EDT (GMT -04:00)

PhD Defence • Artificial Intelligence | Autonomous Vehicles • Empirical Game Theoretic Models for Autonomous Driving: Methods and Applications

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

Atrisha Sarkar, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Krzysztof Czarnecki

Friday, August 5, 2022 11:00 am - 12:00 pm EDT (GMT -04:00)

Master’s Thesis Presentation • Software Engineering • Ape+: A Faster Ape with Static Model Guided Exploration

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

Yaxin Cheng, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Chengnian Sun

Monday, August 8, 2022 10:30 am - 11:30 am EDT (GMT -04:00)

Master’s Thesis Presentation • Cryptography, Security, and Privacy (CrySP) • Contextualizing Alternative Models of Secret Sharing

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

Shannon Veitch, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: University Professor Doug Stinson

Monday, August 8, 2022 2:00 pm - 3:00 pm EDT (GMT -04:00)

Master’s Thesis Presentation • Quantum Computing • Almost Synchronous Correlations Defined within Tracial Von Neumann Algebras

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

Junqiao Lin, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Richard Cleve

Tuesday, August 9, 2022 9:00 am - 10:00 am EDT (GMT -04:00)

Master’s Thesis Presentation • Cryptography, Security, and Privacy (CrySP) • Differential Privacy for Nearest Neighbor Queries

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

Emily Lepert, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Florian Kerschbaum

We examine the problem of providing differential privacy for nearest neighbor queries. Very few mechanisms exist that achieve this, most notable geo-indistinguishability in the context of location privacy. However it uses an extended definition of differential privacy and restricts the sensitivity of queries.