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CIS/IDEAS Seminar: “Flow matching and optimal transport with applications to cell trajectories and protein design”
April 2 at 3:30 PM - 4:30 PM
ABSTRACT:
Flow matching models learn a (possibly stochastic) mapping between source and target distributions. Common paradigms include diffusion models, score matching models, and continuous normalizing flows. In this talk I will first present methods for improved training of flow matching models using ideas from optimal transport. I will then show how these improved methods can be applied to the tasks of (1) modeling cell dynamics, which allow us to better understand disease programs – leading to a new potential therapeutic pathway for triple-negative breast cancer and (2) generative protein design, with applications to biologic drug discovery.
ZOOM LINK: https://upenn.zoom.us/j/94916924500
Alexander Tong
Postdoctoral Researcher, Ph.D.
Alex Tong is a postdoctoral researcher at Mila and Université de Montréal, where he works with Yoshua Bengio at the intersection of generative machine learning and biology with focuses on applications to cells and proteins. This work is part of a joint effort with Fabian Theis through the Helmholtz International Lab. He is also cofounder of Dreamfold, a Mila startup which builds generative models for protein design.
Alex earned his Ph.D. from the Computer Science Department at Yale University in 2021 under the guidance of Smita Krishnaswamy where he studied optimal transport, graph signal processing, and generative modeling of cell dynamics. His research interests span methods such as generative flow modeling, causal discovery, sampling, optimal transport, and their applications in biology.