Research scientist (Directeur de recherche) at CNRS, Nice, France

Vita

I am a CNRS researcher at Laboratoire J. A. Dieudonné of Université Côte d'Azur located in the beautiful city of Nice. I am also part-time professor (PCC) at École Polytechnique. My research interests cover in particular the mathematical foundation of machine learning, especially linked to optimization. In particular, I work on graph machine learning, algorithmic differentiation, (stochastic ∨ bilevel ∨ nonsmooth) optimization. I am currently an Area Chair for NeurIPS, ICML, ICLR, and Action Editor for TMLR. I am also PI of the research projet ANR PRC ANR MAD and 3IA Côte d'Azur chairholder. More information are available on my About page.

Five recent representative publications

See here for a full list.

1. Jérôme Bolte, Tung Lê, Edouard Pauwels, SV. Geometric and computational hardness of bilevel programming. Mathematical Programming. 2025
2. Yann Traonmilin, Rémi Gribonval, SV. A theory of optimal convex regularization for low-dimensional recovery. Information and Inference: A Journal of the IMA 13(2):66pp. 2024.
3. Nicolas Keriven, SV. What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. NeurIPS. 2023.
4. Edouard Pauwels, SV. The Derivatives of Sinkhorn-Knopp Converge. SIAM Journal on Optimization 33(3):1494–1517. 2023.
5. Mathieu Dagréou, Pierre Ablin, SV, Thomas Moreau. A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. NeurIPS. 2022. (Oral paper).