CMOD Seminar with Fabian Fuchs

Our next CMOD seminar is planned for this Thursday July 29th at 9am MDT. Our speaker will be Fabian Fuchs who is currently working with DeepMind and his talk is titled: "Leveraging Symmetries in Machine Learning for Molecules”. 

The talk will be hosted via zoom: //byu.zoom.us/j/6196216115 

Abstract

Understanding proteins is one of several challenges in the natural sciences where machine learning could play a crucial role in enabling breakthroughs with broad societal benefits. What do these challenges have in common, and how do they differ from traditional deep learning applications like vision or NLP? I argue that a crucial aspect is the increased number of task symmetries when going to the length scales of molecules and below. In this talk, I present my PhD research on permutation invariance and roto-translation equivariance. Using the example of tasks on molecules, we examine how to leverage symmetries to solve these tasks efficiently and robustly. I provide a deep dive into the SE(3)-Transformer, but I also cover work on deep learning for sets, and how to leverage symmetries in generative modeling of molecules.

Speaker Bio

I studied physics in Erlangen, at Imperial College, and in Heidelberg. I developed an interest in using computational physics to understand biological processes and wrote my Master thesis on virus self-assembly in hydrodynamic flow. During my PhD, I worked on fundamental machine learning methods, specifically on how to leverage symmetries, often applied to problems in physics and biochemistry. I worked together with Ingmar Posner at the University of Oxford and later started collaborating with Max Welling. I recently interned at DeepMind where I was supervised by Adam Santoro.

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