Lenka Zdeborová

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lenka.zdeborova@epfl.ch +41 21 693 83 27 https://www.epfl.ch/labs/spoc/

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EPFL SB IPHYS SPOC1
BSP 722 (Cubotron UNIL)
Rte de la Sorge
CH-1015 Lausanne

+41 21 693 83 27
Office: BSP 722
EPFL > SB > IPHYS > SPOC1

Web site: Web site: https://www.epfl.ch/labs/spoc/

EPFL IC IINFCOM SPOC2
BC 405 (Bâtiment BC)
Station 14
CH-1015 Lausanne

+41 21 693 83 27
Office: BSP 722
EPFL > IC > IINFCOM > SPOC2

+41 21 693 83 27
Office: BSP 722
EPFL > SB > SB-SPH > SPH-ENS

Web site: Web site: https://sph.epfl.ch/

+41 21 693 83 27
Office: BSP 722
EPFL > VPA > VPA-AVP-PGE > AVP-PGE-EDOC > EDPY-ENS

+41 21 693 83 27
Office: BSP 722
EPFL > IC > IC-SIN > SIN-ENS

Web site: Web site: https://sin.epfl.ch

+41 21 693 83 27
Office: BSP 722
EPFL > IC > IC-SSC > SSC-ENS

Web site: Web site: https://ssc.epfl.ch

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Administrative data

Biography

Lenka Zdeborová is a Professor of Physics and of Computer Science in École Polytechnique Fédérale de Lausanne where she leads the Statistical Physics of Computation Laboratory. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director's Postdoctoral Fellow. Between 2010 and 2020 she was a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay, France. In 2014, she was awarded the CNRS bronze medal, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize, in 2021 the Gibbs lectureship of AMS. She is an editorial board member for Journal of Physics A, Physical Review E, Physical Review X, SIMODS, Machine Learning: Science and Technology, and Information and Inference. Lenka's expertise is in applications of concepts from statistical physics, such as advanced mean field methods, replica method and related message-passing algorithms, to problems in machine learning, signal processing, inference and optimization. She enjoys erasing the boundaries between theoretical physics, mathematics and computer science.

Teaching & PhD

Teaching

Physics

Computer Science
Communication Systems

PhD Students

Behrens Freya, Clarte Lucas Andry, Cui Hugo Chao, Duranthon Odilon Marie Cyprien, Piccioli Giovanni, Troiani Emanuele,

Courses

Machine learning for physicists

* Examples and types of problems that machine learning can solve. * Linear regression in matrix notation. The concept of prediction, estimation. Least squares method. High-dimensional underdetermined problems and the concept of regularization aka ridge regression. Polynomial regression. The concept of bias and variance trade-off and overfitting. Usage of train, validation and test sets.

Statistical physics of computation

Interest in the methods and concepts of statistical physics is rapidly growing in fields as diverse as theoretical computer science, probability theory, machine learning, discrete mathematics, optimization, signal processing and others. Large part of the related work has relied on the use of message-passing algorithms and their connection to the statistical physics of glasses and spin glasses.

Statistical physics for optimization & learning

This course covers the statistical physics approach to computer science problems, with an emphasis on heuristic & rigorous mathematical technics, ranging from graph theory and constraint satisfaction to inference to machine learning, neural networks and statitics.

Lecture series on scientific machine learning

This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the very scientific method.

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