About Me
I am currently at ENS Paris working with with Gabriel Peyré. My research interests include image inverse problems, generative modeling, optimization. I obtained my Ph.D from Université de Bordeaux, co-supervised by Nicolas Papadakis and Arthur Leclaire. I am also one of the main developer of the Python library DeepInv, which provides a unified framework for solving inverse problems using deep neural networks.
Interests
- Deep priors for image inverse problems.
- Theory of denoising diffusion models.
- Non-Euclidean optimization.
- Non-convex convergence of proximal algorithm.
- Reproducible research.
Education
PhD in Machine Learning & Image Processing
University of Bordeaux, France
Master "Mathematiques, Vision, Appentissage" (MVA)
Ecole Normale Superieure Paris-Saclay
Bachelor in Mathematics
Ecole Normale Superieure Paris-Saclay
Recent Publications
(2024).
A relaxed proximal gradient descent algorithm for convergent plug-and-play with proximal denoiser.
In JMIV.
(2023).
Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems.
In Neurips 2023.
(2023).
A relaxed proximal gradient descent algorithm for convergent plug-and-play with proximal denoiser.
In SSVM 2023.
(2023).
Self-Consistent Velocity Matching of Probability Flows.
In Neurips 2023.
(2022).
An Analysis of Generative Methods for Multiple Image Inpainting.
Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging.
(2022).
Proximal denoiser for convergent plug-and-play optimization with nonconvex regularization.
In ICML 2022.
(2021).
Gradient Step Denoiser for convergent Plug-and-Play.
In ICLR 2021.
(2020).
Self-Supervised Small Soccer Player Detection and Tracking.
ACM MMSports 2020.
Recent & Upcoming Talks