Hello there!

I am a PhD student at Télécom Paris and École Polytechnique, supervised by Florence d’Alché-Buc, Rémi Flamary, and Charlotte Laclau.

Research interests

My research focuses on applying deep learning to structured data using differentiable algorithms inspired by optimal transport. Recently I also think a lot about 1) the role symmetries in modern machine learning (bitter lessons or not ?) 2) the opportunities to rejuvenate classical algorithms within the latent space of foundation models and 3) the future of academic research in an agentic world.

News

I will join Pascal Frossard’s LTS4 group at EPFL as a postdoctoral researcher in November 2026.

Recent publications

SOTAlign (ICML 2026) studies the semi-supervised alignment of unimodal foundation models.

This work is a collaboration with Simon Roschmann, Quentin Bouniot, Zeynep Akata (Technical University of Munich), and Sonia Mazelet (École Polytechnique).

GRALE (NeurIPS 2025) is a foundation model for graphs, trained jointly with a differentiable module that learns to solve graph-matching problems.

This work was developed with my colleague Gabriel Mélo and my PhD advisors Florence d’Alché-Buc, Rémi Flamary, and Charlotte Laclau.

Any2Graph (NeurIPS 2024, spotlight) introduces a framework for supervised graph prediction based on a novel optimal-transport loss. It was my first PhD project.

This work was developed with my colleague Junjie Yang and my PhD advisors Florence d’Alché-Buc, Rémi Flamary, and Charlotte Laclau.