Mathis Gerdes

I am a theoretical physicist at MIT, working at the intersection of physics and machine learning. Much of my work turns known physical structure — symmetries, geometry, constraints — into machine-learning methods for problems the theory specifies but leaves out of reach, from gauge theories on the lattice to the geometry of string compactifications. The aim is to make computation a genuine part of theoretical reasoning.

Before my current position at MIT, I did my PhD at the University of Amsterdam with Miranda Cheng and Christoph Weniger.