Florent Krzakala is a professor at Sorbonne Université and a Researcher at Ecole Normale Superieure in Paris. His research interests include Statistical Physics, Machine Learning, Statistics, Signal Processing, Computer Science and Computational Optics. He leads the SPHINX “Statistical PHysics of INformation eXtraction” team in Ecole Normale in Paris, and is the holder of the CFM-ENS Datascience chair and of a PRAIRIE Institute chair. He is also the funder and scientific advisor of the startup Lighton.

**We are organising a school in Les Houches in August 2020 on Statistical Physics of Machine Learning**. If you are a Post-doc or Phd student interested in these topics, consider applying before March 15, 2020.

- Professor UPMC and Researcher at Ecole Normale Superieure, Paris, Since 2013
- Member of the Institut Universitaire de France, Since 2015
- Holder of a Prairie Institute AI Chair, Since 2019
- Member and Fellow of the ELLIS society, Since 2019
- Holder of the chair CFM-ENS on datascience, Since 2016
- Visiting Professor @ Duke University, Maths Dept., 2018
- Visiting Scientist @ Simons Institute in Berkeley, 2016
- Visiting Scientist @ Los Alamos National Labs, 2008
- Maitre de Conference (Associate Professor) in ESPCI Paristech, 2004 - 2013

- Statistical Physics
- Machine learning
- Statistics
- Computer Science
- Random Optimization
- Signal Processing
- Information theory
- Inference on graphs
- Computational optics

Postdoc, 2004

Roma, La Sapienza

PhD in Statistical Physics, 2002

Orsay, Paris XI

MSc in Physics, 1999

Orsay, Paris XI

Current or recent classes

Lecture given in the international master Physics of Complex Systems on computational science

An introductory pratical course by Florent Krzakala and Antoine Baker, Ecole Doctorale EDPIF 2019

Cours Master 1, Université Paris Sorbonne 2019-2010

A set of Lectures given at Duke in 2018 by Lenka Zdeborova and Florent Krzakala

… and where to find them

Quickly discover relevant content by filtering publications.

Detection limits in the spiked Wigner model.
Annals of Statistics.

(2020).
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup.
*Advances in Neural Information Processing Systems 32 (NeurIPS 2019)*.

(2019).
The spiked matrix model with generative priors.
*Advances in Neural Information Processing Systems 32 (NeurIPS 2019)*.

(2019).
Who is Afraid of Big Bad Minima? Analysis of Gradient-Flow in a Spiked Matrix-Tensor Model.
*Advances in Neural Information Processing Systems 32 (NeurIPS 2019)*.

(2019).
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models.
*International Conference on Machine Learning (ICML 2019)*.

(2019).