Data Science

Thematic Program: September 1, 2025 - January 31, 2026

Welcome to the thematic period on Data Science, part of the Excellence Department Project.

This program delves into the intersection of data science and artificial intelligence, offering a unique perspective grounded in statistical physics. Participants will explore cutting-edge methodologies in optimization and control, alongside advanced techniques in numerical simulations and scientific computing for machine learning.

By bridging these fields, the course provides a comprehensive foundation for tackling complex systems and solving real-world problems using innovative, data-driven approaches. The program will include two introductory schools and two workshops.

For further information concerning these events, please see the links below to the corresponding websites.

Program subscription

For the online application we refer to the webpage of the specific event.

Information

Venue and practical information

Schools

Mathematical methods for high-dimensional data

Date: September 08-12, 2025

Venue: Aula Picone, Department of Mathematics, Sapienza University of Rome

Lecturers: Marylou Gabrié (École Normale Supérieure), Silvia Villa (Università di Genova),

Alessandro Ingrosso (Radboud University), Jean Barbier (International Centre for Theoretical Physics)

Abstract: The school will open the thematic period on Data Science and will be dedicated to the mathematical foundations and methods for high-dimensional data analysis. It will provide an in-depth introduction to key mathematical techniques that underpin modern approaches to machine learning and statistical inference. Topics covered will include optimization methods for machine learning, generative modeling and sampling, recurrent neural networks, and high-dimensional Bayesian inference. The school aims to offer students and early-career researchers a solid grounding in these areas, presenting both theoretical developments and practical applications. Furthermore, short talk sessions will provide an opportunity for PhD students and postdocs to share their research and foster new collaborations.

Web Site: link

Organizers: Elena Agliari, Emanuele Caglioti, Alberto Fachechi, Lorenzo Taggi

Numerical methods for high-dimensional data

Date: September 15-19, 2025

Venue: Aula Picone, Department of Mathematics, Sapienza University of Rome

Lecturers: Bruno Després (University Paris 6), Michael Herty (RWTH Aachen University),
George Karniadakis (Brown University)

Abstract: The increasing complexity of modern scientific and engineering problems requires advanced numerical methods capable of handling high-dimensional data efficiently. This school will introduce participants to state-of-the-art techniques in numerical analysis and scientific computing, focusing on challenges arising in high-dimensional settings.

Topics will include model reduction techniques, uncertainty quantification, optimization, and machine learning approaches for solving high-dimensional partial differential equations. The lectures, delivered by leading experts such as Bruno Després, Michael Herty, and George Karniadakis, will provide both theoretical insights and practical applications.

The school is designed for PhD students, postdoctoral researchers, and advanced graduate students interested in high-dimensional computational mathematics. Alongside traditional lectures, hands-on sessions will offer participants the opportunity to implement and test numerical methods on relevant problems.

Web Site: link

Organizers: Alessandro Alla, Davide Torlo, Giuseppe Visconti

Workshops

Optimal control and agent systems

Date: September 22-25, 2025

Venue: Sala di Consiglio, Department of Mathematics, Sapienza University of Rome

Abstract: This workshop explores the intersection of optimal control and agent-based systems, highlighting their role in modern data science applications. We will discuss theoretical advancements, computational methods, and real-world implementations in fields such as reinforcement learning, complex system optimization and data-driven applications. Bringing together leading experts and young researchers, the workshop aims to foster discussions on emerging challenges.

Web Site: link

Organizers: Alessandro Alla, Elisabetta Carlini, Davide Torlo, Giuseppe Visconti
Co-sponsored by: PRIN 2022 PNRR Project (number P2022JC95T) and PRIN 2022 Project (number 2022238YY5)

Statistical methods for machine learning

Date: January 19-20, 2026

Venue: Aula Picone, Department of Mathematics, Sapienza University of Rome

Lecturers: TBA

Abstract: TBA

Web Site: TBA

Organizers: Elena Agliari, Adriano Barra, Fabrizio Durante, Silvio Franz

4th Workshop of UMI Group - Mathematics for Artificial Intelligence and Machine Learning

Date: January 21-23, 2026

Venue: Aula Volterra e Federigo Enriques, Department of Mathematics, Sapienza University of Rome

Plenary Speakers: Paola Antonietti (Politecnico di Milano), Massimo Fornasier (Technische Universität München), Domenico Marinucci (Università degli Studi di Roma Tor Vergata)