About

Hi!

I’m a PhD student at École Polytechnique under the supervision of Maks Ovsjanikov, where I work at the intersection of machine learning, geometry, and physics.

My research combines ideas from dynamical systems, geometric deep learning, and generative modeling to build more interpretable and mathematically grounded neural architectures. I’m particularly fascinated by how concepts from physics can help us understand and improve deep learning systems.

Before starting my PhD, I completed an MSc in Applied Mathematics and Machine Learning (MVA) at ENS Paris-Saclay, and an MSc in Mathematical and Theoretical Physics at the University of Oxford. I also have research experience in computational condensed matter physics and ultrafast spintronics, having worked in research groups at Oxford and Cambridge.

Currently, at École Polytechnique, I’m part of a group exploring geometric and spectral methods for deep learning.

Research Interests

Graph Neural Networks, Generative Models, Mathematical Foundations of Deep Learning, Spectral Methods