Florent Krzakala is a full professor at École polytechnique fédérale de Lausanne in Switzerland. His research interests include Statistical Physics, Machine Learning, Statistics, Signal Processing, Computer Science and Computational Optics. He leads the IdePHIcs “Information, Learning and Physics” laboratory in the Physics and Engineering departments in EPFL. He is also the founder and scientific advisor of the startup Lighton.

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- Full Professor, Physics and EE, EPFL, Switzerland, Since 2020
- Professor UPMC and Researcher at Ecole Normale Superieure, Paris, 2013-2020
- Member of the Institut Universitaire de France, 2015-2020
- Holder of a Prairie Institute AI Chair, 2019-2020
- Member and Fellow of the ELLIS society, Since 2019
- Holder of the chair CFM-ENS on datascience, 2016-2020
- 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

EPFL set of lectures on the theory of statistics, inference, and machine learning.

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

Cours Master 1, Université Paris Sorbonne 2019-2010

Lecture Master 2, Ecole Normale Superieure 2020-2021, ICFP

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

A set of Lectures given at EPFL in 2021 by Lenka Zdeborova and Florent Krzakala

… and where to find them

Quickly discover relevant content by filtering publications.

Mutual Information and Optimality of Approximate Message-Passing in Random Linear Estimation.
*IEEE Transactions on Information Theory*.

(2020).
Kernel Computations from Large-Scale Random Features Obtained by Optical Processing Units.
*ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*.

(2020).
(2020).
On the universality of noiseless linear estimation with respect to the measurement matrix.
*Journal of Physics A: Mathematical and Theoretical*.

(2020).
Detection limits in the spiked Wigner model.
Annals of Statistics.

(2020).