Dorian Baudry

Dorian Baudry

PhD Student

CNRS, Inria, Universite de Lille


I am a PhD student in Machine Learning at CNRS in the Inria ScooL team (formerly SequeL), under the direction of Emilie Kaufmann and Odalric-Ambrym Maillard.

Motivated by practical problems, I explore alternatives to the classical UCB and Thompson Sampling algorithms in Multi-Armed Bandits. I mainly explored two families of algorithms. The first one includes sub-sampling approaches (SDA), allowing for “greedy” comparisons between arms with strong theoretical guarantees in the MAB and non-stationary MAB settings. The second one is a generalization of the Thompson Sampling for bounded distribution, oriented towards practicioners by considering risk-awareness and the question of the sensitivity to model misspecification.

I am also generally interested in reinforcement learning, statistics, and machine learning.


  • Multi-Armed Bandits
  • Statistics
  • Machine Learning


  • PhD Student, 2019-now

    CNRS/Inria Lille, SequeL/ScooL team

  • MSc Mathématiques Vision Apprentissage (MVA), 2019

    ENS Paris-Saclay

  • MSc in Statistics and Computer Science, 2019

    ENSAE Paris



Teaching Assistant

University of Lille

Jan 2020 – Present Lille, France
Teachings include: Data Mining, Natural Language Processing, Mathematics for the Economist.

Data Scientist


May 2019 – Oct 2019 Paris, France
Research project on the analysis of news to predict movements of different assets in the energy market.

Junior Index Structurer

Société générale CIB

Jan 2018 – Aug 2018 London, UK
Pricing of financial products.

Risk Management intern

AXA France

Jun 2017 – Nov 2017 Paris, France
Implementation of a reserving model for long-term care.