Contacts

Carlo Lucibello

Department of Computing Sciences
Associate Professor
Download CV
my personal page

Bio

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.

Research Interests

Neural Networks, Machine Learning, Statistical Inference, Disordered Systems, Statistical Physics, Combinatorial Optimization.

Publications

One-dimensional disordered Ising models by replica and cavity methods
PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR AND SOFT MATTER PHYSICS, 2014
The statistical mechanics of random set packing and a generalization of the Karp-Sipser algorithm
INTERNATIONAL JOURNAL OF STATISTICAL MECHANICS, 2014
Loop 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, 2020
Critical initialisation in continuous approximations of binary neural networks
International Conference on Learning Representations, 2020
One-loop diagrams in the random Euclidean matching problem
PHYSICAL REVIEW. E, 2017
Finite-size corrections to disordered Ising models on random regular graphs
PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR AND SOFT MATTER PHYSICS, 2014
Finite-size corrections to disordered systems on Erdös-Rényi random graphs
PHYSICAL REVIEW. B, CONDENSED MATTER AND MATERIALS PHYSICS, 2013
Anomalous finite size corrections in random field models
JOURNAL OF STATISTICAL MECHANICS: THEORY AND EXPERIMENT, 2014
Scaling hypothesis for the Euclidean bipartite matching problem
PHYSICAL REVIEW E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS, 2014
Loop expansion around the Bethe approximation through the M-layer construction
JOURNAL OF STATISTICAL MECHANICS: THEORY AND EXPERIMENT, 2017
Generalized 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, 2019
Reconstruction of pairwise interactions using Energy-Based Models
Proceedings of Machine Learning Research vol 145: 2nd Annual Conference on Mathematical and Scientific Machine Learning, 9999
Noise cleaning the precision matrix of short time series
PHYSICAL REVIEW. E, 2023
Deep learning via message passing algorithms based on belief propagation
MACHINE LEARNING: SCIENCE AND TECHNOLOGY, 2022
Exponential capacity of dense associative memories
PHYSICAL REVIEW LETTERS, 2024
Information-theoretical analysis of the neural code for decoupled face representation
PLOS ONE, 2024
Neural networks: from the perceptron to deep nets
Spin glass theory and far beyond : replica symmetry breaking after 40 years, 2023
Interpretable pairwise distillations for generative protein sequence models
PLOS COMPUTATIONAL BIOLOGY, 2022
Unexpected upper critical dimension for spin glass models in a field predicted by the loop expansion around the Bethe solution at zero temperature
PHYSICAL REVIEW LETTERS, 2022

Teaching

20883
COMPLEX SYSTEMS AND PHYSICAL MODELS
21013
ARTIFICIAL INTELLIGENCE - MODULE 1
41001
MODERN APPLIED MACHINE LEARNING
21014
ARTIFICIAL INTELLIGENCE - MODULE 2
Past years teaching

Additional teachings info

I’m currently teaching Python programming courses at the Bachelor and MSc level, and Machine Learning courses at the Bachelor and PhD level.