Events

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 seminar will take place in DC 1304.

Silvia Sellán, PhD candidate
Department of Computer Science, University of Toronto

Computer Graphics research has long been dominated by the interests of large film, television and social media companies, forcing other, more safety-critical applications (e.g., medicine, engineering, security) to repurpose Graphics algorithms originally designed for entertainment.

Please note: This talk will take place in DC 1302 and online.

Yan Shvartzshnaider, Assistant Professor
Department of Electrical Engineering and Computer Science
Lassonde School of Engineering, York University

Please note: This PhD seminar will take place online.

Aarti Malhotra, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jesse Hoey

Please note: This PhD seminar will take place online.

Kira Aveline Selby, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Pascal Poupart

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

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

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

Alex Zhang, GFW Report

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.

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 PhD seminar will take place online.

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

Supervisor: Professor Jimmy Lin