MARC JEAN MEZARD
Courses a.y. 2023/2024
41002 STATISTICAL MECHANICS AND METHODS FOR COMPLEX SYSTEMS
I teach quantum and statistical physics at the undergrad level, and a doctoral course on complex systems in information theory, computer science, and physics.
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 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, 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
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