Vincent Cohen-Addad

Vincent Cohen-Addad is a Research Scientist at Google Research with expertise in algorithm design for optimization problems. His work develops efficient methods for clustering, correlation analysis, and private data processing with applications to large-scale machine learning.

He has made significant contributions to coreset frameworks for k-means/k-median problems, differential privacy techniques, and massively parallel clustering algorithms. Recent work includes developing perturbation methods for privacy preservation and sublinear-time algorithms for dynamic clustering.

He holds a CNRS researcher position and previously received a Marie Curie Fellowship at the University of Copenhagen.