CRM: Centro De Giorgi
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The Mathematics of Machine Learning

POSTPONED TO 2023 - Co-founded by PRIN: Gradient flows, Optimal Transport and Metric Measure Structures, DECS: Dipartimento di Eccellenza "Classe di Scienze”, MSCA-IF: Marie Curie Individual Fellowship: OTmeetsDFT

17 January 2022 - 21 January 2022


Additional information on the new Workshop can be retrieved at:

The Workshop "Mathematics of Machine Learning" is associated with the introductory school (10-14 January 2022) and is organized within the framework of the Dipartimenti di Eccellenza "Classe di Scienze”.

The spirit of these activities are related to that of the school: Mathematical and Computational Aspects of Machine Learning organized at Centro De Giorgi in October 2019.

The workshop brings together Machine Learning researchers working on theoretical questions at the interface between mathematics and computer science. The speakers will introduce the mathematical foundations of Machine Learning, discuss the current state of the art but also highlight future research directions and open problems.

The lectures will target a broad audience of early stage mathematicians (Ph.D and advanced Master level) and focus on the following topics:
1) Mathematics of Neural Networks (L. Chizat and A. Montanari)
2) Optimal Transport, Statistics and Machine Learning (P. Rigollet)
3) Optimization for Machine Learning (S. Villa)
4) Applications in Medical Sciences (Jean-Philippe Vert).

In principle, the School will take place at the Centro De Giorgi and the number of participants will be constrained to the capacity limit for this type of event.

The deadline for application is 1st December 2021. For the application procedure, please go to Registration. The confirmation of admission for the workshop will be communicated to registered participants in the first half of December.

The workshop will take place in hybrid mode, with a maximum of 50 persons in presence. Upon reaching this maximum limit, registration will close automatically. In the event of impediments due to new covid-19 pandemic measures, the school will run completely from remote on the same dates.

Online participation requires registration as well. If you plan on attending online, send an email to the address: