Lenka Zdeborová

BSP 722 (Cubotron UNIL)
Rte de la Sorge
1015 Lausanne

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

BC 405 (Bâtiment BC)
Station 14
1015 Lausanne

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

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

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

Administrative data

Teaching & PhD



Computer Science
Communication Systems


Data analysis for Physics

This lecture will introduce the basics of data analysis and learning from data, error estimation and stochasticity in physics. Concepts will be introduced theoretically as well as via numerical exercises done in Python.

Machine learning for physicists

Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practised.

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.