I am a researcher at Inria and Université Grenoble-Alpes, in the GHOST team in Grenoble, which I joined in July 2025.

My research focuses on the theory of reinforcement learning, with a particular interest in posterior sampling and policy gradient methods. I study the exploration and optimization properties of these methods to understand when and how efficiently they learn. My interests also include multi-armed bandits and online learning.

Previously, I was a postdoctoral researcher in the Department of Statistics at the University of Oxford, working with Patrick Rebeschini on online learning and optimization.

Prior to that, I was a postdoctoral researcher at the Inria Fairplay team, based at ENSAE Paris, collaborating with Vianney Perchet. My work focused on online algorithms and bandit models, particularly in structured and constrained problem settings, with practical applications in online advertising and recommendation systems.

I received my PhD in Computer Science from the University of Lille, supervised by Emilie Kaufmann and Odalric-Ambrym Maillard in the Inria ScooL team. My thesis focused on non-parametric algorithms for multi-armed bandits, with applications in agriculture and extensions to risk-aware learning and non-stationary environments. During my PhD, I also spent three months at Kyoto University collaborating with Junya Honda.