Lecture by Lenka Zdeborova from EPFL in Lausanne

Inspired by physics, where simple models are at the core of our theoretical understanding of the physical world, we study simple models of neural networks to clarify some of the open questions surrounding learning with neural networks. In this talk, Lenka Zdeborova describes some of her recent progress in this direction.

Since September 2020 she is an Associate Professor of Physics and of Computer Science and Communication Systems in the Schools of Basic Sciences (SB) and Computer and Communications Sciences (IC) at EPFL in Swiss Lausanne. In 2014, Lenka was awarded the CNRS bronze medal, in 2016 the Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize, in 2020 she delivered the AMS Josiah Willard Gibbs lecture. She is an editorial board member for Journal of Physics A, Physical review E, Physical Review X, SIMODS, and Information and Inference.

Lenka’s expertise is in applications of methods developed in 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.

The lecture was organized by the IMPACT Project (http://impact.ciirc.cvut.cz) and ELLIS Unit Prague.