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Monday, January 29, 2024 10:30 am - 11:30 am EST (GMT -05:00)

Seminar • Machine Learning • Distributionally Robust Machine Learning

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).

Wednesday, January 31, 2024 10:30 am - 11:30 am EST (GMT -05:00)

Seminar • Computer Graphics • Stochastic Computer Graphics

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.

Wednesday, February 7, 2024 9:00 am - 10:00 am EST (GMT -05:00)

PhD Seminar • Bioinformatics • Mitigating the Missing-fragmentation Problem in De Novo Peptide Sequencing with a Two-stage Graph-based Deep Learning Model

Please note: This PhD seminar will take place online.

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

Supervisor: Professor Ming Li

Wednesday, February 7, 2024 10:30 am - 11:30 am EST (GMT -05:00)

Seminar • Artificial Intelligence • Learning Generative Models from a Control Perspective for Scientific Discovery

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.