Flow models with applications to cell trajectories and protein design

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.