## Marc Jean Mezard

I am a Professor of Theoretical Physics. I studied physics at Ecole normale supérieure in Paris and I obtained my PhD in 1984. Hired at CNRS in Paris, I was Research Director in Université Paris Sud. From 2012 and 2022 I became Director of Ecole normale supérieure, and I then joined Bocconi University as a professor, in the newly created department of computational sciences. My work focuses on statistical physics of disordered systems, with applications in various fields like information theory, computer science, machine learning, biophysics.

Creating a largely interdisciplinary new department of computational sciences, with colleagues from mathematics, physics, computer science, computational biology, is an exciting new challenge.

I am interested in the emergent phenomena in complex systems with many interacting “atoms” (that could be for instance agents on a market, information bits, or molecules) that are different or live in different environments. The statistical physics of disordered systems that I contribute to develop finds applications in various branches of science – biology, economics and finance, information theory, computer science, statistics, signal processing. In recent years my research has focused on information processing in neural networks, machine learning and deep networks. I am particularly interested in the theoretical impact of data structure on learning strategies and generalization performance.

## On the nature of the spin glass phase

Phys. Rev. Lett. 52## Replicas and optimization

J. Physique Lett. 46## SK model: the replica solution without replicas

Europhys. Lett. 1 (1985) 77## The space of interactions in neural networks: Gardner's computation with the cavity method

J. Physics A22 (1989) 2181## Epidemic mitigation by statistical inference from contact tracing data

, PNAS (2021 ): 118 (32) e2106548118## Generalization in learning with random features and the hidden manifold model

International Conference of Machine Learning, ICML 2020## Statistical physics-based reconstruction in compressed sensing

Phys. Rev. X 2 (2012) 021005## Reconstruction on trees and spin glass transition

, J. Stat. Phys. 124 (2006) 1317-1350## Analytic and Algorithmic Solution of Random Satisfiability Problems

Science 297 (2002) 812I teach quantum and statistical physics at the undergrad level, and a doctoral course on complex systems in information theory, computer science, and physics.