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Please note: This seminar will take place in DC 1304.

Dinghuai Zhang, PhD candidate
Mila

Advancements in scientific discovery have always been at the forefront of human endeavor, particularly in complex domains such as molecule synthesis. The intrinsic challenges in these fields stem from two main factors: the vast and combinatorially complex high-dimensional search spaces, and the costly evaluation of scientific hypotheses. Therefore, leveraging machine learning offers a promising avenue to expedite the scientific discovery process.

Please note: This seminar will take place in DC 1304.

Arun Jambulapati, Postdoctoral Researcher
Computer Science and Engineering, University of Michigan

Please note: This PhD seminar will take place online.

Nandan Thakur, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jimmy Lin

Please note: This talk will take place in DC 1304 and online.

Alex Zhang, GFW Report

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

Zhenbo Li, Master’s candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Bin Ma, Yang Lu

Please note: This PhD seminar will take place in DC 2310.

Ajay Singh, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Trevor Brown

In this presentation, we introduce Neutralization Based Reclamation (NBR), a novel technique that helps concurrent data structures with non-synchronized traversals to safely free objects. Additionally, we explore optimization possibilities, examining the efficiency of the technique.

Please note: This PhD seminar will take place in DC 3317.

Edward Lee, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Ondřej Lhoták

Reasoning about the use of external resources is an important aspect of many practical applications. Effect systems enable tracking such information in types, but at the cost of complicating signatures of common functions. Capabilities coupled with escape analysis offer safety and natural signatures, but are often overly coarse grained and restrictive.

Please note: This PhD seminar will take place online.

Ruixue Zhang, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Ming Li

Please note: This seminar will take place in DC 1304.

Shiori Sagawa, PhD candidate
Department of Computer Science, Stanford University

Machine learning systems are powerful, but they can fail due to distribution shifts: mismatches in the data distribution between training and deployment. Distribution shifts are ubiquitous and have real-world consequences: models can fail on subpopulations (e.g., demographic groups) and on new domains unseen during training (e.g., new hospitals).

Please note: This PhD defence will take place online.

Siddhartha Sahu, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Semih Salihoğlu