Professor of theoretical physics at ENS Paris (2018-). Research director at IPhT CEA (2002-2018). Associate professor at the Ecole Polytechnique (2010-2015). DIrector of the ICFP Master (2019-), director of the Beg Rohu Summer School (2008-). Editor in Chief of Journal of Statistical Physics. PI of the Simons collaboration «Cracking The Glass Problem», ERC Consolidator Grant 2011, Prix d’Aumale 2018, Young Scientist award in statistical physics 2007.
Topics of interest
Physics, machine learning, complex systems
Project in Prairie
Giulio Biroli will create and promote a strong synergetic activity combining physics and machine learning. He will develop a physically based theoretical approach to deep learning and apply machine learning methods to analyse complex dynamics of physical systems. He will organize a reference annual summer school-workshop on machine learning and physics.
Methodological and conceptual progresses obtained in the last decades in statistical physics and in probability theory, in particular on the analysis of high-dimensional random landscapes, on high-dimensional out of equilibrium dynamics, and on disordered systems, provide theoretical frameworks to tackle difficult challenges in AI. At the same time, AI offers new ways of studying physical systems. This is just the right time to build up on these concurrent opportunities, and develop a strong synergy combining physics and machine learning.