Carlo Lucibello
I am an Associate Professor in the Department of Computing Sciences at Bocconi University. Since 2026, I have served as Director of the Master of Science in Artificial Intelligence.
I earned my PhD in Theoretical Physics from Sapienza University of Rome in 2015 under the supervision of Giorgio Parisi and Federico Ricci-Tersenghi. My doctoral research addressed problems at the intersection of statistical physics and combinatorial optimization.
My research focuses on the theoretical foundations of machine learning and neural networks, with a current emphasis on generative AI. I am also actively involved in open-source software development, contributing to AI libraries for the Julia programming language.
Neural Networks, Machine Learning, Statistical Inference, Disordered Systems, Statistical Physics, Combinatorial Optimization.
One-dimensional disordered Ising models by replica and cavity methods
PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR AND SOFT MATTER PHYSICS, 2014The statistical mechanics of random set packing and a generalization of the Karp-Sipser algorithm
INTERNATIONAL JOURNAL OF STATISTICAL MECHANICS, 2014Loop expansion around the Bethe solution for the random magnetic field Ising ferromagnets at zero temperature
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020Critical initialisation in continuous approximations of binary neural networks
International Conference on Learning Representations, 2020Finite-size corrections to disordered Ising models on random regular graphs
PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR AND SOFT MATTER PHYSICS, 2014Finite-size corrections to disordered systems on Erdös-Rényi random graphs
PHYSICAL REVIEW. B, CONDENSED MATTER AND MATERIALS PHYSICS, 2013Anomalous finite size corrections in random field models
JOURNAL OF STATISTICAL MECHANICS: THEORY AND EXPERIMENT, 2014Scaling hypothesis for the Euclidean bipartite matching problem
PHYSICAL REVIEW E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS, 2014Loop expansion around the Bethe approximation through the M-layer construction
JOURNAL OF STATISTICAL MECHANICS: THEORY AND EXPERIMENT, 2017Generalized approximate survey propagation for high-dimensional estimation
Proceedings of Machine Learning Research. Vol. 97: International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA, 2019Reconstruction of pairwise interactions using Energy-Based Models
Proceedings of Machine Learning Research vol 145: 2nd Annual Conference on Mathematical and Scientific Machine Learning, ForthcomingDeep learning via message passing algorithms based on belief propagation
MACHINE LEARNING: SCIENCE AND TECHNOLOGY, 2022Neural networks: from the perceptron to deep nets
Spin glass theory and far beyond : replica symmetry breaking after 40 years, 2023Interpretable pairwise distillations for generative protein sequence models
PLOS COMPUTATIONAL BIOLOGY, 2022I’m currently teaching Python programming courses at the Bachelor and MSc level, and Machine Learning courses at the Bachelor and PhD level.