Marc Mezard

Full Professor

marc.mezard@unibocconi.it

Bio

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.

About

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

Research Interests

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 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.

Publications

On the nature of the spin glass phase

Phys. Rev. Lett. 52 , 1984

Replicas and optimization

J. Physique Lett. 46 , 1985

SK model: the replica solution without replicas

Europhys. Lett. 1 (1985) 77, 1985

The space of interactions in neural networks: Gardner's computation with the cavity method

J. Physics A22 (1989) 2181, 1989

Epidemic mitigation by statistical inference from contact tracing data

, PNAS (2021 ): 118 (32) e2106548118, 2021

Generalization in learning with random features and the hidden manifold model

International Conference of Machine Learning, ICML 2020, 2020

Modelling the influence of data structure on learning in neural networks: the hidden manifold model

Phys.Rev. X.10.041044, 2019

Statistical physics-based reconstruction in compressed sensing

Phys. Rev. X 2 (2012) 021005, 2012

Threshold values of Random K-SAT from the cavity method

Arxiv, 2006

Reconstruction on trees and spin glass transition

, J. Stat. Phys. 124 (2006) 1317-1350, 2006

Analytic and Algorithmic Solution of Random Satisfiability Problems

Science 297 (2002) 812, 2002

Teaching

41002

STATISTICAL MECHANICS AND METHODS FOR COMPLEX SYSTEMS

30558

STATISTICAL AND QUANTUM PHYSICS

Additional teachings info

I teach quantum and statistical physics at the undergrad level, and a doctoral course on complex systems in information theory, computer science, and physics.