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 and control applications. 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'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 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.
Research interests and projects
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, 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.
- Using the eigenvalue relaxation for binary least-squares estimation problems (with F. Corset). Signal Processing, 89 (11), 2079-2091 (2009).
- On EM algorithms and their proximal generalizations. (with A. O. Hero) ESAIM: Probability and Statistics, 12, 308-326.
- Degeneracy in the maximum likelihood estimation of univariate Gaussian mixtures with EM (with C. Biernacki). Statistics & probability letters, 61(4), 373-382 (2003).
- A component-wise EM algorithm for mixtures (with Celeux, G., Forbes, F., Mkhadri, A.). Journal of Computational and Graphical Statistics (2002).
- Kullback proximal algorithms for maximum-likelihood estimation (with A. Hero). IEEE transactions on information theory, 46(5), 1800-1810 (2000).
See Google Scholar for a full publication list or here. The list of my recent talks is here.
Lecture notes, courses given, ... are here
Tel: 020 8943 6051
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