He is a postdoctoral researcher at the Centre de Mathématiques Appliquées (CMAP) at École Polytechnique, where he is a member of the HPC@Maths team.

He recently completed his PhD in Reinforcement Learning at Université Paris-Saclay, focusing on learning-based control of dynamical systems with applications in computational fluid dynamics. His research was supervised by A. Vilnat, O. Semeraro, and L. Mathelin.

Previously, he was a research intern at Inria TAU, where he worked on learning-based methods for stiff differential equations, including Koopman operator theory and Physics-Informed Neural Networks, under the supervision of M.-A. Bucci, T. Faney, C. Mehl, and M. Schoenauer.

He has also completed research internships in several R&D departments, including Capital Fund Management (CFM), where he worked on anomaly detection in time series; BNP Paribas Real Estate, where he applied machine learning to real estate market analysis; and Luxurynsight, where he developed deep learning methods for natural language processing.

He holds an MSc in Artificial Intelligence, Systems, and Data, an MSc in Statistics and Financial Mathematics, and a BSc in Applied Mathematics (Probability and Statistics), all from Université Paris Dauphine – PSL

• Reinforcement Learning
• Learning-based Control
• Information Theory
• Physics-informed Machine Learning