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Flow Models with Applications to Cell Trajectories and Protein Design

Alex Tong, MILA and
Wednesday, March 06, 2024
12:00 pm - 1:00 pm
Alex Tong, MILA and
Duke Computer Science Colloquium

Lunch will be served at 11:45 AM

Generative flow 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 models using flow matching objectives using ideas from optimal transport. I will then show how these improved methods can be applied to the tasks of (1) modelling 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.

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.