Évènements

Évènements à venir

Avis de soutenance de thèse de M. Thibault Moquet

Avis de Soutenance Monsieur Thibault MOQUET Mathématiques appliquées Soutiendra publiquement ses travaux de thèse intitulés Algorithmes de Frank–Wolfe et dualité en optimisation convexe non lisse avec applications au contrôle à champ moyen dirigés par Monsieur Guilherme MAZANTI et Monsieur Laurent PFEIFFER Soutenance prévue le lundi 1er décembre 2025 à 13h30Lieu : 9 bis rue Joliot […]

01/12/2025

Bienvenue – Novembre 2025

Nous avons le plaisir d’accueillir : M. BERRY Jules (postdoctorant), M. BRAIK Zeidan (doctorant), Mme HAMROUNI Naoures (doctorante), M. LUONG Alain (doctorant), Mme MEENA Preeti (postdoctorante), M. NGUYEN Quoc Duong (doctorant), M. ORTIZ PEREZ (postdoctorant), Mme YE Zixin (postdoctorante)

30/11/2025

SCube seminar – Scott Pesme- Deep learning theory through the lens of diagonal linear networks

November 28, 2025 — 11:00 ABSTRACT : Surprisingly, many optimisation phenomena observed in complex neural networks also appear in so-called 2-layer diagonal linear networks. This rudimentary architecture—a two-layer feedforward linear network with a diagonal inner weight matrix—has the advantage of revealing key training characteristics while keeping the theoretical analysis clean and insightful. In this talk, […]

28/11/2025

Predictive Control Strategies for Electric Traction, Wind Energy, and Smart Grid Applications

Thursday 27th November 2025 at 2pm, in Salle Hooper, 3rd floor at IBM building, 14 Rue Jean Rostand, 91400 Orsay Salle Hooper,3rd floor at IBM building,14 Rue Jean Rostand, 91400 Orsay Speakers: Alfeu J. Sguarezi Filho (Federal University of ABC, Brasil) Title: Predictive Control Strategies for Electric Traction, Wind Energy, and Smart Grid Applications Abstract: This presentation aims […]

27/11/2025

SCube seminar – Alex Rodrigo dos Santos Sousa – On Bayesian wavelet shrinkage estimation of nonparametric regression models with stationary errors

November 21, 2025 — 14:00 Abstract This work proposes a Bayesian rule based on the mixture of a point mass function at zero and the logistic distribution to perform wavelet shrinkage in nonparametric regression models with stationary errors (with short or longmemory behavior). The proposal is assessed through Monte Carlo experiments and illustrated with real […]

21/11/2025