Amine Bennouna

I am a 4th 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 challenges in data-driven decision-making. My recent work focuses on designing novel robust approaches to Machine Learning problems (and more generally Stochastic Optimization) using Distributionally Robust Optimization. The goal is to understand what are the sources of overfitting in data-driven stochastic optimization and design robust approaches precisely pretecting against such sources. These approaches are typically provably optimal (i.e the best that can be done with the data) when a certain out-of-sample guarantee is desired. Earlier in my PhD, I have also worked in Reinforcement Learning (RL) to introduce a novel more efficient off-line RL framework. We have then applied this framework to COVID-19 predictions and vaccine allocation. 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.