Events

Monday, June 26, 2023 — 8:30 AM to 11:30 AM EDT

PhD Defence • Artificial Intelligence • The Impact of Teams in Multiagent Systems

David Radke, PhD candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Kate Larson, Tim Brecht

Tuesday, June 27, 2023 — 1:00 PM to 2:00 PM EDT

Master’s Thesis Presentation • Systems and Networking • Kernel- vs. User-Level Networking: A Ballad of Interrupts and How to Mitigate Them

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

Peter Cai, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Martin Karsten

Tuesday, June 27, 2023 — 1:00 PM to 2:00 PM EDT

PhD Seminar • Cryptography, Security, and Privacy (CrySP) • Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration

Please note: This PhD seminar will take place online.

Miti Mazmudar, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Ian Goldberg

Tuesday, July 4, 2023 — 9:00 AM to 12:00 PM EDT

PhD Defence • Human-Computer Interaction • Investigating New Forms of Single-handed Physical Phone Interaction with Finger Dexterity

Please note: This PhD defence will take place in DC 1331 and virtually.

Yen-Ting (Allen) Yeh, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Daniel Vogel

Monday, July 10, 2023 — 1:00 PM to 2:00 PM EDT

Master’s Thesis Presentation • Human-Computer Interaction • Eggly: Designing Mobile Augmented Reality Neurofeedback Training Games for Children with Autism Spectrum Disorder

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

Yue Lyu, Master’s candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Jian Zhao, Keiko Katsuragawa

Tuesday, July 18, 2023 — 3:00 PM to 6:00 PM EDT

PhD Defence • Machine Learning | Natural Language Processing • Less is More: Restricted Representations for Better Interpretability and Generalizability

Zhiying (Gin) Jiang, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jimmy Lin

In this thesis, we aim at improving interpretability and generalizability through restricting representations. We choose to approach interpretability by focusing on attribution analysis to understand which features contribute to prediction on BERT, and to approach generalizability by focusing on effective methods in low-data regime.

Thursday, July 20, 2023 — 9:00 AM to 12:30 PM EDT