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CEREMADE - UMR 7534
Nos dernières publications
Tropp 8/9 - Random matrix theory and computational linear algebra
This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both
Tropp 9/9 - Random matrix theory and computational linear algebra
This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both
Tropp 7/9 - Random matrix theory and computational linear algebra
This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both
Bubeck 9/9 - Some geometric aspects of randomized online decision making
This course is concerned with some of the canonical non-stochastic models of online decision making. These models have their origin in works from the 1950's and 1960's, and went through a resurgence
Carpentier - Introduction to some problems of composite and minimax hypothesis testing
A fundamental question in statistics is: how well can we fulfil a given aim given the data that one possesses? Answering this question sheds light on the possibilities, but also on the fundamental
Bubeck 8/9 - Some geometric aspects of randomized online decision making
This course is concerned with some of the canonical non-stochastic models of online decision making. These models have their origin in works from the 1950's and 1960's, and went through a resurgence
Tropp 6/9 - Random matrix theory and computational linear algebra
This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both
Bubeck 6/9 - Some geometric aspects of randomized online decision making
This course is concerned with some of the canonical non-stochastic models of online decision making. These models have their origin in works from the 1950's and 1960's, and went through a resurgence
Bubeck 7/9 - Some geometric aspects of randomized online decision making
This course is concerned with some of the canonical non-stochastic models of online decision making. These models have their origin in works from the 1950's and 1960's, and went through a resurgence
Tropp 5/9 - Random matrix theory and computational linear algebra
This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both
Bubeck 5/9 - Some geometric aspects of randomized online decision making
This course is concerned with some of the canonical non-stochastic models of online decision making. These models have their origin in works from the 1950's and 1960's, and went through a resurgence
Zdeborová - Loss landscape and behaviour of algorithms in the spiked matrix-tensor model
A key question of current interest is: How are properties of optimization and sampling algorithms influenced by the properties of the loss function in noisy high-dimensional non-convex settings?
Intervenants et intervenantes
Titulaire d'un doctorat en Mathématiques (Lille 1, 2010)
Professeur adjoint à l'université de Princeton, département de la recherche opérationnelle et de l'ingénierie financière
Mathématicienne. Professeure, responsable de l'équipe Mathematical statistics and machine learning, Institut für Mathematik, Universität Potsdam (2024)
Titulaire d'un doctorat en Mathématiques appliquées. Informatique (Lille 1, 2012)
Membre du jury d'une thèse en Mathématiques à Université Côte d'Azur en 2025
Mathématicien. Professeur des universités en mathématiques, Institut Camille Jordan (ICJ, UMR 5208), École centrale de Lyon (2025)
Membre junior 2022 de l'Institut universitaire de France
Titulaire d'un doctorat en mathématiques (Toulouse 3, 2011)
Maître de conférences à l'Université Paris X, Nanterre, FR (2016). Professeure associée au département Systèmes d’Information, Sciences de la Décision et Statistiques de l'ESSEC Business School (2023).
Informaticien. Directeur de recherche INRIA, membre de l'équipe-projet Apprentissage, graphes et optimisation distribuée (ARGO), Département d'informatique (DI-ENS, UMR 8548), École normale supérieure, Paris. Délégué scientifique du Centre INRIA de Paris (2026)
Titulaire d'un doctorat de Mathématiques, Université Paris-Sud 11, Orsay (en 2008). En poste à l'INRA, SupAgro (en 2014)
Physicienne. Professeure associée, directrice du Laboratoire de physique statistique des systèmes computationnels - Statistical physics of computation laboratory (SPOC), Institut de physique (IPHYS), École polytechnique fédérale, Lausanne, Suisse (2024)
Doctorat en physique théorique (Paris 11 en cotutelle avec Universita Karlova v Praze, 2008)
Médaille de bronze du CNRS 2018