Summary
Ayman Chaouki is an AI researcher and PhD candidate jointly affiliated with École Polytechnique and the University of Waikato, specializing in optimal decision-tree discovery within reinforcement learning. He designs algorithms spanning dynamic programming, branch-and-bound, and Monte Carlo tree search that come with optimal convergence and finite-time PAC-style guarantees. His work balances theoretical rigor—aiming to generalize to MDPs and study sample complexity—with applied experience in deep RL for portfolio optimization and fraud detection. With a decade of industry and research experience across institutions like Télécom Paris, CFM, and HrFlow.ai, he brings practical impact (publications and workshop presentations) to cutting-edge theory. Based in Paris, he also has a track record translating numerical methods across languages and improving industrial ML systems, reflecting both mathematical depth and engineering versatility.
10 years of coding experience
3 years of employment as a software developer
Master of Science - MS, M.V.A. (Mathematics, Vision and Learning), Master of Science - MS, M.V.A. (Mathematics, Vision and Learning) at ENS Paris-Saclay
Engineer's degree, Applied Mathematics, Engineer's degree, Applied Mathematics at CentraleSupélec
Classes préparatoires aux grandes écoles (CPGE), Classes préparatoires aux grandes écoles (CPGE) at Lycée Moulay Youssef Rabat
Doctor of Philosophy - PhD, Applied Mathematics, Doctor of Philosophy - PhD, Applied Mathematics at The University of Waikato
Doctor of Philosophy - PhD, Applied Mathematics, Doctor of Philosophy - PhD, Applied Mathematics at École Polytechnique
English, French, Arabic