Graduate Student

Sorbonne Université, M.Sc. Pure Mathematics (2023)
École Normale Supérieure, M.Sc. Theoretical Physics (2022)
Jacobs University Bremen, B.Sc. Physics (2020)

J. Bayer, C. Benzmüller, K. Buzzard, M. David, L. Lamport, Yu. Matiyasevich, L. Paulson, D. Schleicher, B. Stock and E. Zelmanov, "Mathematical Proof Between Generations," Notices of the AMS, Vol. 71, 1 (2024).

M. David and F. Méhats, "Symplectic Learning for Hamiltonian Neural Networks," J. of Comp. Phys. 494, 112495 (2023).

F. Fehse, M. David, M. Pioro-Ladrière and W. A. Coish, "Generalized...

I am a PhD candidate in Physics at the University of California, Berkeley. My research focuses on leveraging advanced machine learning techniques, particularly Generative Models, to analyze particle collider data. My research projects, including Moment Unfolding and Infinite Deconvolution, demonstrate the application of these models to complex datasets, advancing our understanding of particle physics and showcasing their broad applicability.

My academic foundation was laid at Yale, where I earned my BS and MS degrees in Mathematics and Physics with Distinction, completing my studies...