I'm a Research Fellow at the Center for Computational Mathematics of the Flatiron Institute in New York, and member the of Polymathic AI collaboration. I received my Ph.D. in Computer Science from the Physics Lab of Ecole Normale Supérieure in 2022, and was a research scientist for the startup LightOn where I worked on developing new machine learning algorithms and applications (adversarial robustness, differential privacy, time-series prediction...) for Optical Processing Units. During my PhD, I interned at the Criteo AI Lab.

My research currently focuses on building foundation models for scientific data (mostly numerical simulations of partial differential equations and astrophysics data), as well as dataset building and on training large deep learning models, as part of the Polymathic AI collaboration.

Before my Ph.D., I graduated with an engineering degree from ESPCI Paris, an MSc in Condensed Matter from Ecole Normale Supérieure, and an MSc in Statistics from Sorbonne University. During these undergraduate studies, I interned at NTT Basic Research Laboratory in Japan, the LIGO at M.I.T. and the Quantum Information Group at the LIP6 at Sorbonne University.

Research

The Well: a large-scale collection of physics numerical simulations for machine learning

We will soon release one of the most extensive collection of scientific datasets, which comprises numerical simulations of diverse physics phenomena ranging from biology to astrophysics. We hope it will advance both the developement of ML surrogate models, accelerate scientific discovery, and allow the training of foundation models.

Blind denoising using Gibbs-Diffusion.

We developed a very cool blind denoising algorithm for natural images, with an application in astrophysics. It can at the same time do parameter inference with uncertainties. We believe it has the potential to be applied in many different scientific fields. The project was accepted at ICML 2024 and the code can be found here.

Publications

Talks

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