Events tagged with Thesis defence

Friday, June 14, 2024 — 1:00 PM to 2:00 PM EDT

Master’s Thesis Presentation • Human-Computer Interaction • Memolet: Reifying the Reuse of User-AI Conversational Memories

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

Hen-Chen Yen, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jian Zhao

Tuesday, June 18, 2024 — 11:00 AM to 12:00 PM EDT

Master’s Thesis Presentation • Cryptography, Security, and Privacy (CrySP) • Efficient Memory Allocator for Restricting Use-After-Free Exploitations

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

Ruizhe Wang, Master’s candidate
David R. Cheriton School of Computer Science

Supervisors: Professors N. Asokan, Meng Xu

Thursday, June 27, 2024 — 11:00 AM to 2:00 PM EDT

PhD Defence • Computer Vision | Machine Learning • Unsupervised Losses for Clustering and Segmentation of Images: Theories & Optimization Algorithms

Please note: This PhD defence will take place online.

Zhongwen (Rex) Zhang, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Yuri Boykov

Friday, June 28, 2024 — 2:00 PM to 3:00 PM EDT

Master’s Thesis Presentation • Quantum Computing • Quantum Query Complexity of Hypergraph Search Problems

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

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

Supervisor: Professor Shalev Ben-David

Tuesday, July 2, 2024 — 10:30 AM to 11:30 AM EDT

Master’s Thesis Presentation • Data Systems • Triangle Count Estimation and Label Prediction Over Uncertain Streaming Graphs

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

Ipsita Mohanty, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor M. Tamer Özsu

Thursday, July 4, 2024 — 11:00 AM to 12:00 PM EDT

Master’s Thesis Presentation • Human-Computer Interaction • Technology Design Recommendations Informed by Observations of Videos of Popular Musicians Teaching and Learning Songs By Ear

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

Christopher Liscio, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Dan Brown

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