Samuel Vaiter

Samuel Vaiter is a CNRS Researcher affiliated with the Laboratoire J. A. Dieudonné at Université Côte d'Azur in Nice, France. His work bridges mathematics and computer science with a primary focus on theoretical machine learning foundations.

His research centers on optimization theory applied to machine learning, particularly in graph-based systems and automatic differentiation. Key projects include the ANR-funded MAD (Mathematics of Automatic Differentiation) and BOGL (Bilevel Optimization for Graph Learning), which develops algorithms for graph classification, link prediction, and community detection while addressing non-Euclidean data structures. His work emphasizes rigorous mathematical frameworks for contemporary AI challenges.

Vaiter serves as Area Chair for premier conferences including NeurIPS, ICML, ICLR, and AISTATS, demonstrating significant community leadership. His research is conducted within the Laboratoire J. A. Dieudonné, a mathematics-focused research unit at Université Côte d'Azur.