Florent Krzakala

Florent Krzakala is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Physics Department (PHY). His research interests include statistical physics, machine learning, and inference. He teaches courses such as Fundamentals of Inference and Learning, Statistical Physics, and Statistical Physics for Optimization & Learning. Krzakala's work focuses on the application of statistical physics to computer science problems, including graph theory, constraint satisfaction, and machine learning. He supervises PhD students and is associated with the IDEPHICS laboratory at EPFL.

Florent Krzakala is a researcher at École polytechnique fédérale de Lausanne with expertise in Statistical Mechanics, Statistics, Machine Learning, Information theory, and Spin Glasses. His work explores the intersection of statistical physics and machine learning, focusing on inference problems, phase transitions, and algorithmic applications. Key contributions include studies on stochastic block models, compressed sensing, and neural network learning dynamics. His research also addresses challenges in sparse network analysis, optimization, and the behavior of deep learning models. Krzakala's publications highlight the use of statistical-physics methods to understand learning and inference in high-dimensional settings.