RESEARCH:

The goal of Marco Biroli’s research is to develop physically inspired machine learning models, drawing on advances at the interface of statistical physics, stochastic processes, and deep learning. The aim is to devise algorithms that respect underlying physical constraints (e.g., symmetries, conserved quantities, stochastic dynamics), thereby improving interpretability, generalization, and robustness.

BIO:

Marco Biroli is an Eric and Wendy Schmidt AI in Science Fellow at UChicago under the supervision of Professor Vincenzo Vitelli. His research interests lie in non-equilibrium statistical physics, with particular focus on extreme value statistics of strongly correlated variables and interacting particle systems. He holds a Master’s degree in Theoretical Physics from École Normale Supérieure, Paris, and a Bachelor of Science with a double major in Mathematics and Physics from École Polytechnique, Palaiseau.