The “Mathematical Foundations of AI” day, organized jointly by the DATAIA Institute and SCAI, in association with scientific societies: the Fondation Mathématique Jacques Hadamard (FMJH), the Fondation Sciences Mathématiques de Paris-FSMP, the MALIA group of The French Statistical Society and the Société Savante Francophone d'Apprentissage Machine (SSFAM) aims to provide an overview of some promising research directions at the interface between statistical learning and AI.
For this new edition, a particular focus will be on a "hot topic" area both in the learning and applied mathematics community, PINNs (Physically Informed Neural Networks). The morning will be devoted to plenary presentations given by renowned researchers and specialists in the subject. The afternoon will be an opportunity for young researchers to present their work via posters or short talks.
Marianne Clausel (Univ. Lorraine)
Emilie Chouzenoux (INRIA Saclay, DATAIA Institute)
Stéphan Chrétien (Univ. Lyon 2)
Sylvain Le Corff (Sorbonne University)
Konstantin Usevich (CNRS)
Myriam Tami (CentraleSupélec)
9am-10am | Keynote 1: C. Boyer (Sorbonne University, IUF)
10 a.m.-10:45 a.m. | Coffee break
10:45 a.m. - 12 p.m. | Short contributory talks
12 p.m.-1:45 p.m. | Lunch break
1:45 p.m.-2:45 p.m. | Keynote 2: E. Franck (INRIA Grand Est)
2:45 p.m.-3:30 p.m. | Snack break
3:30 p.m.-4:30 p.m. | Keynote 3: T. Silvetti Falls (CentraleSupélec, University of Paris-Saclay)
4:30 p.m.-5:30 p.m. | Short contributory talks
As part of this workshop, participants are invited to submit a detailed abstract for a possible oral presentation or poster presentation. During the selection process, the committee is very attentive to giving the best possible visibility to doctoral students, researchers or teacher-researchers. When submitting your application by email (maths-ia@inria.fr), please indicate the following elements: surname/first name, institution, status, title/abstract.
Financial assistance for the mission may be granted by the committee upon justified request.
Deadline to apply: December 31, 2023.