Carlo Lucibello
I am an Assistant Professor of Computer Science at Bocconi University. I earned my PhD in Physics in 2015 from Sapienza University, under the supervision of Giorgio Parisi and Federico Ricci-Tersenghi. My research leverages analytical methods from statistical physics to deepen the theoretical understanding of machine learning and neural networks. I develop efficient, physics-inspired algorithms for learning, optimization, and inference.
Research interests
Neural Networks, Machine Learning, Statistical Inference, Disordered Systems, Statistical Physics, Combinatorial Optimization.
Selected Publications
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, 2022Teaching
41001
MODERN APPLIED MACHINE LEARNING
30586
MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE LAB
I’m currently teaching Python programming courses at the Bachelor and MSc level, and Machine Learning courses at the Bachelor and PhD level.