Presented By: Frontiers in Scientific Machine Learning (FSML)
Frontiers in Scientific Machine Learning (FSML) Seminar: Alexander Tong (Post-doctoral Fellow, Mila - Quebec AI Institute)
Flow matching in cell trajectories and protein design
Meeting Link:
https://umich.zoom.us/j/97823527756?pwd=H01BbvtuG5q02Wzb8LJvhUnvijlAIe.1
Abstract:
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.
Bio:
Alexander Tong is a postdoctoral fellow at Mila with Yoshua Bengio, visiting researcher at Oxford with Michael Bronstein, cofounder of Dreamfold—a protein design startup, and incoming assistant professor at Duke University starting July 2025. Alex completed his Ph.D. in Computer Science at Yale University in 2021 with Smita Krishnaswamy. His research interests span generative modeling, graph signal processing, and optimal transport to understand biological systems with a focus on cells and proteins.
https://umich.zoom.us/j/97823527756?pwd=H01BbvtuG5q02Wzb8LJvhUnvijlAIe.1
Abstract:
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.
Bio:
Alexander Tong is a postdoctoral fellow at Mila with Yoshua Bengio, visiting researcher at Oxford with Michael Bronstein, cofounder of Dreamfold—a protein design startup, and incoming assistant professor at Duke University starting July 2025. Alex completed his Ph.D. in Computer Science at Yale University in 2021 with Smita Krishnaswamy. His research interests span generative modeling, graph signal processing, and optimal transport to understand biological systems with a focus on cells and proteins.
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