I am a 3rd year doctoral candidate at the MIT Operations Research Center, advised by Prof. B. Van Parys. Prior to joining MIT, I graduated from Ecole Polytechnique in 2019 Majoring in Applied Mathematics and completed two years of preparatory program (CPGE) in Lycée Louis-le-Grand.
My research interest lies in data-driven decision-making. I strive to understand theoretically how to use most efficiently observed data to make decisions. The goal is to derive from these theoretical insights novel algorithms that tackle unsolved challenges in data-driven decision-making. So far in my PhD, I have worked in dynamic decision-making (Reinforcement Learning) to introduce a novel more efficient off-line RL framework. I have also worked on designing decision-making schemes, using Distributionally Robust Optimization , that are provably optimal (i.e the best that can be done with the data) when a certain out-of-sample guarantee is desired. Finally, I worked on analyzing the representation power of neural networks and contributing to explain theoretically why neural networks work so well in data-driven prediction.