Stephane Chretien

Senior Research Scientist - National Physical Laboratory

Stephane Chretien studied at Ecole Normale Superieure (Cachan) and obtained his PhD in Electrical Engineering from the Universite Paris Sud Orsay in 1996, where he worked on successive projection methods for nonconvex set theoretic feasibility problems in signal processing. He then joined the University of Michigan (Ann Arbor) as a post-doctorate in Alfred Hero's group 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 as a researcher in the NUMOPT team lead by Claude Lemarechal at INRIA where he studied numerical methods for nonsmooth optimization and EM-types algorithms for clustering with the group of Gilles Celeux. In 1999, he joined Martine Labbe's Mathematics for Decision 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 Universite de Franche Comte, Besancon where he studied efficient algorithms for Compressed Sensing, time series analysis and clustering, and contributed theoretical results on sparse recovery and finite random matrices. He joined the National Physical Laboratory (Mathematics and Modelling) in September 2015. He is now with the newly created Data Science Division, working on medical and industrial applications. His research focuses on time series analysis, machine learning, clustering, image segmentation, genetics, scheduling, combinatorial optimization, etc. He also offers consultancy in all potential technical challenges for the industry, involving high dimensional statistics, compressed sensing, large scale deterministic and stochastic optimisation.

New ! I recently joined the team at Verso - optim, as start-up doing optimisation for logistics, routing, scheduling, and many other things based on efficient and bespoke integration of combinatorial optimisation routines that will "smooth the worflow" !

My CV is here.

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