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

Franceand in theUK. 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 theEuropeanresearch 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 the 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 Americaand inEurope. I am also a member of the European cooperation for statistics of network data science action which brings togetherEuropeanexperts 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

USas well as inEuropeand 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

List of former and current PhD students:

**Papers**

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].

L. Bergé, C. Bouveyron, C. Corneli, P. Latouche. "The latent topic block model for the co-clustering of textual interaction data". In : Journal of Computational Statistics and Data Analysis (2019), p. 247-270 [web].

G. Fouetillou, P. Latouche, C. Bouveyron, and P-A. Mattei. "Exact dimensionalality selection for Bayesian PCA". In : Journal of Statistical Planning and Inference (2019), in press [web].

M. Corneli, C. Bouveyron, P. Latouche, and F. Rossi. "The dynamic stochastic topic block model for time evolving networks with textual edges". In : Statistics and Computing (2019), in press.

P. Latouche, C. Bouveyron, and P-A. Mattei. "Bayesian variable selection for globally sparse probabilistic PCA". In : Electronic Journal of Statistics 12.2 (2018), p. 3036-3070 [web].

R. Rastelli, P. Latouche, and N. Friel. "Choosing the number of groups in a latent stochastic block model for dynamic networks". In : Network Science 6.4 (2018), p 469-493 [web].

P. Latouche, S. Robin, and S. Ouadah. "Goodness of fit of logistic regression models for random graphs". In : Journal of Computational and Graphical Statistics (2018), p. 98-109 [web].

M. Corneli, P. Latouche, and F. Rossi. "Multiple change points detection and clustering in dynamic networks". In : Statistics and Computing (2018), p. 989-1007 [web].

P. Latouche, C. Bouveyron, D. Marié, and G. Fouetillou. "Présidentielle 2017 : l’analyse des tweets renseigne sur les recompositions politiques". In : Statistique et Société 5.3 (2017) [web].

J. Wyse, N. Friel, and P. Latouche. "Inferring structure in bipartite networks using the latent block model and exact ICL". In : Network Science 5.1 (2017), p. 45-69 [web].

R. Zreik, P. Latouche, and C. Bouveyron. "The dynamic random subgraph model for the clustering of evolving networks". In : Computational Statistics (2016), p. 1-33 [web].

P ; Latouche and S. Robin. "Variational Bayes model averaging for gryphon functions and motifs frequencies inference in W-graph models". In : Statistics and Computing 26.6 (2016), p. 1173-1185 [web].

P. Latouche, P-A Mattei et al. "Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high dimension regression". In : Journal of Multivariate Analysis 146 (2016), p. 177-190 [web].

M. Corneli, P. Latouche, and F. Rossi. "Exact ICL maximisation in a non stationary temporal extension of the stochastic block model for dynamic networks". In : Neurocomputing 192 (2016), p. 81-91 [web].

M. Corneli, P. Latouche, and F. Rossi. "Block modelling in dynamic networks with non homogenous Poisson processes and exact ICL". In : Social Network Analysis and Mining 6.1 (2016), p. 55-85 [web].

C. Bouveyron, P. Latouche, and R. Zreik. "The stochastic topic block model for the clustering of vertices in networks with textual edges". In : Statistics and Computing (2016), p. 1-21 [web].

R. Zreik, P. Latouche, and C. Bouveyron. "Classification automatique de réseaux dynamiques avec sous-graphes : étude du scandale Enron". In : Journal de la Société Française de Statistique 156.3 (2015), p. 166-191 [web].

E. Côme and P. Latouche. "Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood". In : Statistical Modelling 15.6 (2015), p. 564-589 [web].

P. Latouche, E. Birmelé, and C. Ambroise. "Model selection in overlapping stochastic block models". In : Electronic Journal of Statistics 8.1 (2014), p. 762-794 [web].

Y. Jernite, P. Latouche et al. "The random subgraph model for the analysis of an ecclesiastical network in Merovingian Gaul". In : Annals of Applied Statistics 8.1 (2014), p. 377-405 [web].

P. Latouche, E. Birmelé, and C. Ambroise. "Variational Bayesian inference and complexity control for stochastic block models". In : Statistical Modelling 12.1 (2012), p. 93-115 [web].

P. Latouche, E. Birmelé, and C. Ambroise. "Overlapping stochastic block models with application to the French political blogosphere". In : Annals of Applied Statistics 5.1 (2011), p. 309-336 [web].

C. Bouveyron, M. Corneli, P. Latouche. "Co-clustering of ordinal data via latent continuous random variables and a classification EM algorithm".

N. Jouvin, P. Latouche, C. Bouveyron. "Clustering of count data through a mixture of multinomial PCA".

R. Zreik, C. Ducruet, C. Bouveyron, and P. Latouche. "Cluster dynamics in the collapsing Soviet shipping network". In : Advances in Shipping Data Analysis and Modeling Tracking and Mapping Maritime Flows in the Age of Big Data. Routledge (2017) [web].

R. Zreik, P. Latouche, C. Bouveyron, and C. Ducruet. "Cluster identification in maritime flows with stochastic methods". In : Maritime Networks : Spatial Structures and Time Dynamics. Routledge (2015) [web].

P. Latouche, E. Birmelé, and C. Ambroise. "Overlapping clustering methods for networks". In : Handbook of Mixed Membership Models and Their Applications. Chapman et Hall/CRC (2014) [web].

P. Latouche, E. Birmelé, and C. Ambroise. "Bayesian methods for graph clustering". In : Advances in Data Handling and Business Intelligence". Springer (2009) [web].

P. Latouche, C. Bouveyron, D. Marié, G. Fouetillou. "Présidentielle 2017 : une réorganisation politique du web social ?". In Data analytics post (2017).

P. Latouche, C. Bouveyron, D. Marié, G. Fouetillou. "Présidentielle 2017 : une réorganisation politique du web social ?". Panthéon-Sorbonne magazine (2017).

P. Latouche, C. Bouveyron. "Les échanges de données au peigne fin". CNRS, le journal (2017).

P. Latouche, C. Bouveyron. "Des réseaux, des textes, et de la statistique !". Lettre de l’INSMI (2016).

- 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

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