I’m currently an assistant professor at the MAP5 laboratory and the STID departement of IUT de Paris, which is part of Université de Paris. I’ve done a PhD in AgroParisTech supervised by Céline Lévy-Leduc, Julien Chiquet and Laure Sansonnet. After thtat, I was in a postdoctoral position, between Sorbonne Université and Toulouse institut of mathematics, mentored by Etienne Roquain and Pierre Neuvial.
Research: My PhD titled Regularized methods to study multivariate data in high dimensional settings : theory and applications aims at selecting variable of interest in the multivariate linear model when the number of responses is much higher than the sample size. My postdoc aims at finding upper bounds of the number of false discoveries for all the possible sets of selected variables in a multiple testing framework. These upper bounds have to be valid with high probabilities. Most of the existing bounds are too conservative and a crucial challenge is to build new post hoc bounds with better performances.
Association As a member of the young group of the French statistical society I co-organised a meeting of the young European statistician, a session of the JDS (French statistical days) 2019, and one session and two workshops for the JDS 2020.
PhD in Statistics, 2016 - 2019
AgroParisTech / Université Paris-Saclay
Statistician Diploma, 2012-2015
Classe Préparatoire, 2010-2012
In this article we used statistical model to described rules that explain communication between dendritic cells and T helper cells. This rules can be helpful in vaccine design and immunotherapy.
Theoretical, numerical and applied studies of a novel variable selection approach in the framework of multivariate linear models taking into account the dependence that may exist between the responses. In this framework the number of responses can be much higher than the sample size.
Theoretical study of a novel variable selection approach in the framework of multivariate linear models taking into account the dependence that may exist between the responses. In this study the number of responses can tend to infty as a power of the sample size.