Matthieu Wyart

Matthieu Wyart is a Full Professor of Theoretical Physics at École polytechnique fédérale de Lausanne (EPFL), holding a position in the School of Basic Sciences within the Institute of Physics. He leads research in the Physics of Complex Systems Laboratory (PCSL) at EPFL, where he investigates fundamental questions at the intersection of condensed matter theory, statistical mechanics, and emerging connections to machine learning.

Wyart completed his education at prestigious French institutions, earning his physics degree with Honors from École Polytechnique in Paris in 2001, followed by a Diploma of Advanced Studies in Theoretical Physics with highest Honors from École Normale Supérieure, Paris in 2002. He obtained his doctoral degree in Theoretical Physics and Finance from SPEC, CEA Saclay, Paris in 2006 with a thesis on electronic markets. His academic journey included postdoctoral positions at Harvard University, Janelia Farm, and Princeton University before joining New York University as an Assistant Professor in 2010, where he was promoted to Associate Professor in 2014. He moved to EPFL in July 2015 as an Associate Professor of Theoretical Physics and was promoted to Full Professor in April 2024.

His research spans multiple domains including condensed matter theory, statistical mechanics, quantum information, and biophysics, with particular focus on disordered systems, glass transitions, amorphous solids, and the emerging connections between physical systems and machine learning architectures. Wyart's work often reveals deep theoretical connections between seemingly disparate fields, such as demonstrating how principles governing amorphous materials relate to the behavior of neural networks. His recent publications explore hierarchical structures in data, diffusion models, learning curves for compositional data, and the physics of creep in disordered media.

Through his laboratory (PCSL), Wyart fosters interdisciplinary research that bridges traditional physics with contemporary challenges in machine learning and complex systems. His work has established important theoretical frameworks for understanding the glass transition, jamming phenomena, and the geometric principles underlying both physical and artificial learning systems.