I am a PhD student at Inria Lille, under the supervision of Pr. Olivier Pietquin (Google Brain) and Pr. Romuald Élie (DeepMind). Before starting my PhD, I graduated from École Polytechnique.

My research focuses on applying Reinforcement Learning to solve Mean Field Games. Mean Field Games theory studies decision making in a population of an infinite number of identical agents. It has inspired numerous applications such as population dynamics modeling, crowd motion, economics or energy management and production.

In my spare time, I like to play and compose electronic music. I also enjoy outdoor activities, especially skiing and surfing.


  • Reinforcement Learning
  • Mean Field Games
  • Game Theory


  • PhD in Machine Learning, 2019 - 2022

    Scool (ex. SequeL) Inria / Univ. Lille

  • MSc. in AI & Computer Vision, 2018 - 2019

    École Polytechnique

  • Engineering Degree, 2015 - 2018

    École Polytechnique



Software Engineer Intern

YouTube (Google)

Apr 2019 – Aug 2019 Paris, France
Machine Learning to improve video classification and recommendation system (C++, Python).

Research Intern

SRI International AI Center

Apr 2018 – Jul 2018 Menlo Park, California

Under the supervision of Dr. Rodrigo de Salvo Braz.

  • Symbolic Parameter Estimation in Probabilistic Graphical Models.
  • Implementation in PRAiSE & Expresso software (Java).



Jun 2017 – Aug 2017 Stockholm, Sweden

SCiBreak is a startup in the Energy sector which develops high-voltage fast-acting circuit breakers.

  • Image processing (Python, OpenCV).
  • Design and topological optimization (Autodesk Inventor, SolidWorks).

Recent & Upcoming Talks

Mean Field Games Flock! The Reinforcement Learning Way

Reinforcement Learning for Mean Field Games

Mean Field Games & Reinforcement Learning