I am Professor of Applied Mathematics at the MAP5 laboratory of the University of Paris. My group is involved in problems related to learning and statistics. Until 2018, I was Assistant Professor and then Associate Professor at Université Paris 1 Panthéon-Sorbonne. I both studied in France and in the UK. I am a reviewer for journals and conferences including JASA, PNAS, NeurIPS, ICML, JRSS-B, JRSS-C. I serve as an associate editor for the Journal of the Royal Statistical Society. I am an expert for the European research council. I co-invented and developed the Linkage software. I teach statistics and machine learning at the University of Paris and Ecole Polytechnique. In particular, I am involved in the master Artificial Intelligence & Advanced Visual Computing. Moreover, I am the director of a bachelor in data science at the University of Paris. I am also responsible along with N. Chopin of the probabilistic graphical models course of the master MVA of ENS Paris Saclay.
I am a member of the international working group on model based clustering created by Adrian Raftery from the University of Washington. Meetings are hold regularly in north America and in Europe. I am also a member of the European cooperation for statistics of network data science action which brings together European researchers of the field.
I like to work in an interdisciplinary environment and to be involved in problems raised by teams with a different background than mine. For instance, I have worked with biologists, historians, doctors, and sociologists. More recently, I have been particularly interested in studying customers products relationships in recommendation systems. Moreover, I have worked on developing algorithms to automatically extract the hidden structures of complex systems and organizations such as companies.
I am also deeply involved in the valorization of the algorithms / software I develop. I have patents in the US as well as in Europe and I am involved in entrepreneurial projects.
My research interests include:
* Statistical learning on networks and heterogenous data
* Statistical learning in high dimensions
* Deep graphical modelling / Deep learning
* Structural decision making
M. Corneli, C. Bouveyron, P. Latouche. "Co-clustering of ordinal data via latent continuous random variables and a classification EM algorithm". In: Journal of Computational and Graphical Statistics (2020), in press [web].
S. Ouadah, S. Robin, and P. Latouche. "A degree-based goodness-of-fit test for heterogeneous random graph models". In: Scandinavian Journal of Statistics (2019), in press [web].
- Linkage (plate-forme web) : Analysis of networks with textual edges
- Spinyreg (R package) : spare regression using spike and slab prior distributions
- GofNetwork (R package) : assess the goodness of fit of network models in the presence of covariates
- Mixer (R package written in C++) : variational inference techniques for the
stochastic bloc model. Can be used to classify the vertices of a network depending on their connection profiles
- Rambo (R package) : estimate the parameters, the number of classes and cluster vertices of a random network into groups with homogeneous connection profiles. The clustering is performed for directed graphs with typed edges (edges are assumed to be drawn from multinomial distributions) for which a partition of the vertices is available
- Netlab (Matlab) : some of the most important pattern recognition algorithms described by C.M. Bishop in “Neural Networks for Pattern Recognition” (Oxford University Press, 1995)
- Genoscript (WebObject) : a Web environment for transcriptom analysis