Marie-Laure Martin-Magniette is a senior researcher at INRA at the interface between statistics and molecular biology. She is strongly involved in the analyses of genomic data. Since 2003, she has acquired a strong expertise on the data normalization and the differential analysis for microarray and High-Throughput Sequencing technologies. She also investigated the analysis of chIP-chip data to detect enriched regions and differentially methylated regions. Since 2005 she has been focused on the discovery and characteristics of underlying structures in genomic data with mixture models and Hidden Markov Models. She conceived these models in close collaboration with fellow biologists and statisticians. Since September 2013, she has led the team Genomic Networks of the Institute of Plant Sciences Paris-Saclay. Her team project is highly interdisciplinary and deals with the construction of genomic networks of the plant model Arabidopsis thaliana for the discovery of functional modules and the prediction of functions of orphan genes involved in stress responses.
Stéphane Robin started his career as an assistant professor at AgroParisTech and is now senior researcher at INRA-AgroParisTech. His researches are dedicated to the development of statistical methodologies with emphasize on models with latent variable (EM algorithm, variational approximation), graphical models, change point detection and network modelling. These developments are often motivated by applications in life sciences, more specifically molecular biology or bioinformatics and, more recently, ecology. Stephane Robin has made particularly strong contributions for network analysis. He was one the pioneers who considered variational techniques for the inference of network models in the early 2000. Since then, his many contributions facilitated the development of research teams in France specialized in the domain. He is part of the European Cooperation for Statistics of Network Data Science.
Charles Bouveyron is Professor of Statistics at University Côte d’Azur, Nice, France, and holds the Chair Inria in Data Sciences. He received in 2006 the Ph.D. degree from University Grenoble 1 (France) for his work on high-dimensional classification. In 2006–2007, he was a postdoctoral researcher in the Department of Mathematics and Statistics of Acadia University in Canada where he worked on the statistical analysis of networks. Then, he was Assistant Professor (2007-2012) and Associate Professor (2012-2013) at University Paris 1 Panthéon-Sorbonne. From 2013 to 2017, he was Professor of Statistics and head of Department of Statistics at University Paris Descartes, Paris, France. His research interests include classification of high-dimensional data, classification under uncertainty and weak supervision, adaptive and online learning as well as network analysis. He has become a recognized expert in model-based classification (EM algorithm, latent variable, etc.) and analysis of high-dimensional data (latent subspaces, variable selection, intrinsic dimension estimation, etc.). In this context, he has developed several innovative clustering and classification methods and applied them with success in medical imaging, mass spectrometry and chemometrics.
Pierre Latouche is Professor of Statistics at University Paris Descartes and Ecole Polytechnique, Paris, France. He received in 2011 the Ph.D. degree from University Evry (France) for his work on network modeling and analysis. He was Assistant Professor (2011-2017) and Associate Professor (2017-2018) at University Paris 1 Panthéon-Sorbonne. His research interests include network analysis, sparse inference, high-dimensional data, graphical models, Bayesian analysis and variational approaches. Pierre Latouche started his work on networks in the mid 2000. He is interested in both methodological and theoretical aspects. He developed the overlapping stochastic block model which allows to look for overlapping clusters of nodes. More generally, over the years, he has proposed many extensions to the original stochastic block model. In particular, he is one the inventors of the linkage methodology. Pierre Latouche is also part of the European Cooperation for Statistics of Network Data Science.