CS Colloquium Speaker

Speaker: Silvia Sellán, University of Toronto

Date: Monday, March 25

Time: 12:30pm EST

Location: CS 105

Host: Adam Finkelstein

Event page: https://www.cs.princeton.edu/events/26599

Title: Stochastic Computer Graphics

Abstract: 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. In this talk, I will advocate for a perspective shift in our field that allows us to design algorithms directly for these safety-critical application realms. I will show that this begins by reinterpreting traditional Graphics tasks (e.g., 3D modeling and reconstruction) from a statistical lens and quantifying the uncertainty in our algorithmic outputs, as exemplified by the research I have conducted for the past five years. I will end by mentioning several ongoing and future research directions that carry this statistical lens to entirely new problems in Graphics and Vision and into specific applications.

Bio: Silvia is a fifth year Computer Science PhD student at the University of Toronto, working in Computer Graphics and Geometry Processing. She is a Vanier Doctoral Scholar, an Adobe Research Fellow and the winner of the 2021 University of Toronto Arts & Science Dean’s Doctoral Excellence Scholarship. She has interned twice at Adobe Research and twice at the Fields Institute of Mathematics. She is also a founder and organizer of the Toronto Geometry Colloquium and a member of WiGRAPH.

CS Colloquium Speaker

Speaker: Shiori Sagawa, Stanford University

Date: Tuesday, March 26

Time: 12:30pm EST

Location: CS 105

Host: Ellen Zhong

Event page: https://www.cs.princeton.edu/events/26601

Title: Distributionally Robust Machine Learning

Abstract: Machine learning models are widely deployed today, but they can fail due to distribution shifts: mismatches in the data distribution between training and deployment. Models can fail on certain subpopulations (e.g., language models can fail on non-English languages) and on new domains unseen during training (e.g., medical models can fail on new hospitals). In this talk, I will discuss my work on algorithms for improving robustness to distribution shifts. First, to mitigate subpopulation shifts, I develop methods that leverage distributionally robust optimization (DRO). My methods overcome the computational and statistical obstacles of applying DRO on modern neural networks and on real-world shifts. Second, to tackle domain shifts, I build WILDS, a benchmark of real-world shifts, and show that existing methods fail on WILDS even though they perform well on synthetic shifts from prior benchmarks. I then develop a state-of-the-art method that successfully mitigates real-world domain shifts; my method proposes an alternative to domain invariance—a key principle behind the prior methods—to reflect the structure of real-world shifts. Altogether, my algorithms improve robustness to a wide range of distribution shifts in the wild, from subpopulation shifts in language modeling to domain shifts in wildlife monitoring and histopathology.

Bio: Shiori Sagawa is a final-year PhD Candidate in Computer Science at Stanford University, advised by Percy Liang. Her research focuses on algorithms for reliable machine learning. She was awarded the Stanford Graduate Fellowship and an Apple Scholars in AI/ML PhD Fellowship. Prior to her PhD, she received her B.A. in Computer Science and Molecular and Cell Biology from UC Berkeley, and she worked at D. E. Shaw Research.