Emmanuel Rachelson

Emmanuel Rachelson is a Professor of Machine Learning and Optimization at ISAE-SUPAERO. His research focuses on Reinforcement Learning and Sequential Decision Problems, with broader interests in Machine Learning and Operations Research. He co-founded the SuReLI (Supaero Reinforcement Learning Initiative) and contributes to the "Machine Learning, Decision, Optimization" research group within the Department of Complex Systems Engineering (DISC).

His work emphasizes practical applications in aerospace engineering, including robotic control, fault detection in avionic systems, and aerodynamic optimization. He pioneered the Data & Decision Sciences MS curriculum and continues coordinating its machine learning components, while also exploring theoretical aspects like robust MDPs, successor state representations, and exploration-driven learning strategies.

Recent research trends include graph-based state representation, regularization techniques for CNNs, and evolutionary policy search balancing diversity and quality. His methodological contributions span mathematical optimization, temporal coordination under uncertainty, and simulation-based approaches for complex decision processes.