Regular biography
Lenka Zdeborová is an Associate Professor in the Physics Department at École Polytechnique Fédérale de Lausanne, where she leads the Statistical Physics of Computation Laboratory. Her research focuses on the application of statistical physics concepts to machine learning, signal processing, inference, and optimization. She holds a PhD in physics from the University of Paris-Sud and Charles University in Prague. Zdeborová has held positions at the Los Alamos National Laboratory and CNRS, and she is affiliated with the SPOC laboratory. She teaches courses on data analysis for physics, scientific machine learning, and machine learning for physicists.
Scholar-generated biography
Lenka Zdeborová is a researcher at EPFL, Switzerland, with expertise in statistical physics, learning theory, phase transitions, deep learning, and high-dimensional statistics. Her work explores the intersection of machine learning and physical sciences, focusing on inference, clustering, and network analysis. She has contributed to understanding phase transitions in modular networks, spectral methods for clustering sparse networks, and the statistical-physics-based reconstruction in compressed sensing. Her research also includes the analysis of deep neural networks, learning dynamics, and generalization errors in high-dimensional settings. Zdeborová's publications highlight the application of statistical physics to problems in machine learning and data science.