Leonardo De Carlo

Leonardo De Carlo is an Assistant Professor (Professor Auxiliar) of Mathematics at the Departamento de Engenharia Informática e Sistemas de Informação, Universidade Lusófona, Lisbon. He is also an integrated researcher at the Grupo de Física Matemática (GFM) at Instituto Superior Técnico (IST), Lisbon, and a collaborator at COPELABS (ILIND), a research unit within Universidade Lusófona. His interdisciplinary work bridges mathematical physics, statistical mechanics, and data science.

Education:

• Doctor of Philosophy in Mathematics, Scuola Internazionale Superiore di Studi Avanzati (SISSA), 2017
• Laurea Specialistica in Physics, Dipartimento di Fisica, "La Sapienza", Università di Roma, 2012

His research focuses on non-equilibrium statistical mechanics, hydrodynamics of particle systems, wavefunction physics, and mathematical structures in chaotic dynamics. Recently, he has expanded into statistical learning algorithms and AI for weather forecasting. His work combines rigorous mathematical analysis with computational modeling.

The analysis of his recent publications reveals a strong emphasis on the microscopic origins of macroscopic behavior, particularly in non-equilibrium settings. Themes include hydrodynamic limits, current fluctuations, quantum-inspired stochastic models, and geometric structures in dynamical systems. There is a clear trajectory from fundamental physics to applied machine learning, especially in modeling complex systems through both analytical and data-driven approaches.

Scientific Affiliations and Research Units:

• Grupo de Física Matemática (GFM), IST Lisbon (FCT-funded)
• COPELABS (ILIND), Universidade Lusófona
• Center of Technology and Systems (CTS)

Leonardo has advised no publicly listed students yet. His research has been supported through institutional affiliations and FCT funding. He previously worked as a postdoctoral researcher at Instituto Superior Técnico, Scuola Normale Superiore, and LUISS Guido Carli, and briefly in industry at CINECA as an HPC Data Engineer on AI-driven weather forecasting. He has also taught at the high school level in both public and private sectors. His future research aims to unify insights from statistical physics with modern machine learning techniques.