Frank Noé: Biophysics at the Age of AI

Frank is a computational biologist on the interface of molecular simulation, statistics and machine learning. His main research interests are in the development of deep learning methods to simulate and understand molecular systems. Frank has co-pioneered the Markov state modeling approach for describing the long-time dynamics of proteins and other macromolecules, has developed several deep learning systems for molecular simulation, such as the Boltzmann Generator, and promotes open research and software for academia and society. 

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