Cérémonie de la remise des Prix mathématiques François Ducrot le 21 septembre 2023

Contenu de la page:

6 Vidéos (Alice Gionnet et 5 des alumni) et des photos de la cérémonie.
Livret de présentation des lauréat.e.s.

Soutenir ce prix

La marraine : Alice Guionnet

Directrice de recherche au CNRS à l’ENS de Lyon
Médaille d’argent du CNRS 2010
Membre de l’académie des sciences 2017
Oratrice au congrés international des mathématiques 2022

Cadeau mathématique :

Vidéos des présentations des alumni

Annabelle Beaudoin

Master Data Science 2022
Biostatisticienne AP-HP Paris

Clémence Bouvier

Licence de math à Angers 2015-2018
Master Crypto à Rennes 2018-2020
Thèse 2023 INRIA Paris

L2 et L3 math à Angers 2007-2009
Ecole polytechnique 2009-2013 puis ENSAE
Actuaire 2016-2019
Finance à Paris

Delphine Pol

Licence 3 puis master MFA 2010-2013
Thèse en 2013-2017 à Angers
Post-doc au Japon puis Allemagne 2017-2021
Consultante TNG Technology consulting en Allemagne

Philippine Renaudin

Master Data Science 2021
Consulting Data performance chez Treez data Management à Nantes

Photos des lauréat.e.s avec alumni

Merci aux mécènes

Merci aux mécènes

Livret du prix avec les profils des récipiendaires et des alumni

Projet-livret_220923 Télécharger

Nos 31 récipiendaires 2023 : 2 en masters, 9 en L2 et 20 en L1

Aubril Ony M1 MFA	Crequy Tudal M 1 Data Science	Wahl Noé L3 Math	Aubril Salomé L2 math
Fromentin Théo L3 Math	Rousseau Tom L2 Math	Crapsky Yann L2 Math	Gonin Arnaud L3 Maths Appliquées
Blanchard Gabriel L3 Math	Morand Hugo DL 3 Math-éco	Aliprantis Daphné DL1 Math-info	Fardin Amandine DL1 Math-éco
Loreau Unger Maxime, DL1 Math-info	Boeri Aurélien DL1 Math-info	Fu Mike DL1 Math-info	Marhin Aydan DL1 Math-info
Boeuf Léonard DL1 Math-info	Giroire Maelys DL1 Math-info	Morille Emanuel DL1 Math-info	Boidron Justine L1 Math
Herbert Bastien L1 Math	Nedeljkovitch Félicia DL1 Math-éco	Cocault Elise DL1 Math-info	Imedjoubene Lydia DL1 Math-info
Regert Jonathan DL 1 Math-info	Da Silva David DL1 Math-info	Lahaye Emma DL1 Math-éco	Rouat Amélie PPPE 2
De Caestecker Noé DL1 Math-info	Le Bigot Alice PPPE 1	Schneider Yalalt DL1 Math-éco

Les entreprises qui nous soutiennent

Les sociétés savantes qui nous soutiennent

Soutiens institutionels

Séminaire des doctorant.es
In this presentation, I focus on the semiclassical Schrödinger equation, a fundamental equation in quantum mechanics that describes the evolution of quantum particles over time. Since exact solutions to this equation are rarely explicit and conventional numerical methods are often impractical, my goal is to develop approximate solutions that are both easier to compute and accurate. To achieve this, I study special functions called wave packets, which represent localized quantum states. First, I will present how, starting from initial data defined by a wave packet, we can construct a good approximate solution using a wave packet, for the scalar semiclassical Schrödinger equation. Then, I will explain how this approach can be extended to more complex vector-valued systems, where new phenomena arise.

Séminaire des doctorant.es
In this talk, we will explore the behavior of a random walk when conditioned to remain within a cone. We will begin by introducing the problem of conditioning a random walk to never escape a cone. To address this, we will examine the concept of the Doob h-transform and its role in shaping the walk's behavior. Next, we will delve into the set of harmonic functions associated with random walks and discuss their significance in the context of conditioning the walk. Finally, we will derive the conditions under which a unique discrete harmonic function exists for a Dirichlet problem posed within a cone under certain assumptions about the transition kernel of the random walk and the cone itself.

Séminaire des doctorant.es
Inference of the tail parameters of a distribution is a question of interest. Indeed, some extreme events can have disastrous consequences and being able to estimate their probability of appearance allows us to prevent them. It is however a difficult question because usual statistics theory does not work well in that case. Extreme value theory has been developed for this purpose. In particular, the Conditional Tail Moments (CTMs) are useful tools in risk quantification. For instance, the Expected Shortfall (ES), a particular case of CTM, is a risk measure widely used in finance. The estimation of CTMs and the demonstration of convergence results on these estimators have been the purpose of my first year of PhD. In this talk, I will start with an introduction and motivation to Extreme Value Theory. I will then define the Conditional Tail Moment and give the mathematical framework in which estimation of extreme CTM is manageable. Finally, if time permits, I would like to present some of the convergence results me and my PhD supervisors have been able to produce so far.