Stephane Chretien studied at Ecole Normale Supérieure (Cachan) and obtained his PhD in Electrical Engineering from the Université Paris-Sud, Orsay, in 1996, where he developed new projection methods for nonconvex set theoretic feasibility problems in signal processing and control applications. He continued as a postdoctorate in Alfred Hero's group at the University of Michigan where here developed a Kullback-Proximal framework for the analysis of estimation algorithms in statistics and machine learning with application to Positron Emission Tomography. He then went back to France and joined the NUMOPT team lead by Claude Lemarechal at INRIA where he studied EM-types algorithms for clustering, and stochastic algorithms for nonsmooth convex optimization. In 1999, he joined Martine Labbe's Mathematics for Decision's team in Brussels, where he studied network flow problems and convex relaxations for urban traffic modelling and control. In 2000, he was appointed Assistant Professor in the Mathematics Laboratory (Probability and Statistics team) at the Université de Franche Comté, Besançon, where he developed efficient algorithms for compressed sensing, time series analysis and clustering and contributed theoretical results on sparse recovery and finite random matrices. He joined NPL (Mathematics & Modelling) in September 2015.
Stephane's research interests are in computational statistics, big data, machine learning, compressed sensing optimisation. He has worked on various projects in time series analysis, machine learning, clustering, image segmentation, genetics, scheduling and combinatorial optimization; and has been funded via both industrial and academic grants. He also offers consultancy in all potential technical challenges for the industry, involving high dimensional statistics, compressed sensing, large scale deterministic and stochastic optimisation.