Ayman Chaouki
PhD Candidate
Paris, Ile-de-France
Summary
👤
SeniorAyman 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
Github Skills (18)
programming-language10
machine-learning10
deep-q-learning10
science10
julia10
signal-processing9
numerical9
julialang9
hpc9
actor-critic9
deep-reinforcement-learning7
tensorflow6
pytorch6
dqn5
branch-and-bound4
Programming languages (3)
Github contributions (5)
Chaoukia/Reinforcement-Learning-course
Mar 2023 - Jun 2026
Contributions:2 PRs, 50 pushes, 3 branches in 3 years 3 months
Chaoukia/branches
Jun 2024 - Nov 2025
Branches algorithm, fast AO* search for optimal sparse Decision Trees.
Contributions:18 pushes, 1 comment in 1 year 5 months
decision-treebranch-and-bounddecision-trees-learningdynamic-programmingoptimal-decision-trees
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.