What I am all about.

Starting from september 2017, I'm Professor of Statistics (*Professeur des Universités*) at Université Côte d'Azur, Nice, France.
I hold the chair of excellence INRIA on "Data Science". I'm both a member of the
laboratoire J.A. Dieudonné, UMR CNRS 7135, and of the team Epione
in INRIA Sophia-Antipolis. I'm also an associate editor for The Annals of Applied Statistics
and the founding organizer of the series of Statlearn workshops. I'm also the director of the
Master of Science in "Data Science and Artificial Intelligence" of Université Côte d'Azur.

Previously, I was Professor at University Paris Descartes (2013-2017) where I was heading the department of Statistics of the laboratoire MAP5. I was also a member of the steering commitee of the "Fondation des Siences Mathématiques de Paris" and the president of the "Statistics and Image" group of the French Statistical Association SFDS.

My research interests include:

- Statistical learning (clustering, classification, regression) in high dimensions,

- Adaptive statistical learning (uncertain labels, evolving distributions, novelty detection, ...),

- Statistical learning on networks, functional data and heterogeneous data,

- Applications of statistical learning (medicine, image analysis, chemometrics, humanities, ...).

What I do.

I build the real value.

The SaaS plateform Linkage.fr implements a clustering techniques for networks with textual edges. You can analyze with Linkage networks such as email networks or co-authorship networks. Linkage allows you to upload your own network data or to make requests on scientific databases (Arxiv, Pubmed, HAL).

Batman would be jealous.

- C. Bouveyron, M. Corneli and P. Latouche, *Co-Clustering of ordinal data via latent continuous random variables and a classification EM algorithm*, Preprint HAL 01978174, Université Côte d’Azur, 2019: [pdf].

- A. Saint-Dizier, J. Delon and C. Bouveyron, *A unified view on patch aggregation*, Preprint HAL 01865340, Université Côte d’Azur, 2018: [pdf].

- C. Bouveyron, L. Cheze, J. Jacques, P. Martin and A. Schmutz, *Clustering multivariate functional data in group-specic functional subspaces*, Preprint HAL 01652467, Université Côte d'Azur, 2017: [pdf].

- L. Bergé, C. Bouveyron, M. Corneli and P. Latouche, *The Latent Topic Block Model for the Co-Clustering of Textual Interaction Data*, Computational Statistics and Data Analysis, in press, 2019: [pdf].

- C. Bouveyron, P. Latouche and P.-A. Mattei, *Exact Dimensionality Selection fo Bayesian PCA*, Scandinavian Journal of Statistics, in press, 2019: [pdf].

- F. Orlhac, P.-A. Mattei, C. Bouveyron and N. Ayache, *Class-specific Variable Selection in High-Dimensional Discriminant Analysis through Bayesian Sparsity*, Journal of Chemometrics, in press, 2019: [web] [pdf].

- C. Bouveyron, M. Corneli, P. Latouche and F. Rossi, *The dynamic stochastic topic block model for dynamic networks with textual edges*, Statistics and Computing, in press, 2019: [pdf].

- C. Bouveyron, J. Delon and A. Houdard, *High-Dimensional Mixture Models for Unsupervised Image Denoising (HDMI)*, SIAM Journal on Imaging Sciences, vol. 11(4), pp. 2815–2846, 2018: [web] [pdf].

- C. Bouveyron, P. Latouche and P.-A. Mattei, *Bayesian Variable Selection for Globally Sparse Probabilistic PCA*, Electronic Journal of Statistics, vol. 12(2), pp. 3036-3070, 2018: [web] [pdf].

- J. Ulloa, T. Aubin, D. Llusia, C. Bouveyron and J. Sueur, *Estimating animal acoustic diversity in tropical environments using unsupervised multiresolution analysis*, Ecological Indacators, vol. 90, pp. 346-355, 2018: [web]

- C. Bouveyron, L. Bozzi, J. Jacques and F.-X. Jollois, *The Functional Latent Block Model for the Co-Clustering of Electricity Consumption Curves*, Journal of the Royal Statistical Society, Series C, vol. 67(4), pp. 897-915, 2018: [web] [pdf].

- C. Bouveyron, P. Latouche and R. Zreik, *The Stochastic Topic Block Model for the Clustering of Networks with Textual Edges*, Statistics and Computing, vol. 28(1), pp. 11-31, 2017: [web] [pdf].

- C. Bouveyron, G. Fouetillou, P. Latouche & D. Marié, *Présidentielle 2017 : l’analyse des tweets renseigne sur les recompositions politiques*, Statistique et Société, vol. 5(3), pp. 39-44, 2017: [web].

- C. Bouveyron, P. Latouche and R. Zreik, *The Dynamic Random Subgraph Model for the Clustering of Evolving Networks*, Computational Statistics, vol. 32(2), pp. 501-533, 2017: [web] [pdf].

- C. Bouveyron, G. Hébrail, F.-X. Jollois and J.-M. Poggi, *Un DU d’Analyste Big Data en formation continue courte, au niveau L3*, Statistique et Enseignement, vol. 7 (1), pp. 127-134, 2016: [web].

- C. Bouveyron, J. Chiquet, P. Latouche and P.-A. Mattei, *Combining a Relaxed EM Algorithm with Occam's Razor for Bayesian Variable Selection in High-Dimensional Regression*, Journal of Multivariate Analysis, vol. 146, pp. 177-190, 2016: [web] [pdf].

- C. Bouveyron, M. Fauvel and S. Girard, *Parsimonious Gaussian process models for the classification of hyperspectral remote sensing images*, IEEE Geoscience and Remote Sensing Letters, vol. 12, pp.2423-2427, 2015: [web] [pdf].

- C. Bouveyron, E. Côme and J. Jacques, *The discriminative functional mixture model for a comparative analysis of bike sharing systems*, The Annals of Applied Statistics, vol. 9 (4), pp. 1726-1760, 2015: [web] [pdf].

- C. Bouveyron, P. Latouche and R. Zreik, *Classification automatique de réseaux dynamiques avec sous-graphes : étude du scandale Enron*, Journal de la Société Française de Statistique, vol.156(3), pp. 166-191, 2015: [web] [pdf].

- C. Bouveyron, M. Fauvel and S. Girard, *Kernel discriminant analysis and clustering with parsimonious Gaussian process models*, Statistics and Computing, vol. 25(6), pp. 1143-1162, 2015: [web] [pdf].

- C. Bouveyron, L. Jegou, Y. Jernite, S. Lamassé, P. Latouche & P. Rivera, *The random subgraph model for the analysis of an ecclesiastical network in merovingian Gaul*, The Annals of Applied Statistics, vol. 8(1), pp. 377-405, 2014: [web] [pdf].

- C. Bouveyron, *Adaptive mixture discriminant analysis for supervised learning with unobserved classes*, Journal of Classification, vol. 31(1), pp. 49-84, 2014: [web] [pdf].

- C. Bouveyron and C. Brunet, *Model-based clustering of high-dimensional data: A review*, Computational Statistics and Data Analysis, vol. 71, pp. 52-78, 2014: [web] [pdf].

- C. Bouveyron and J. Jacques, *Adaptive mixtures of regressions: Improving predictive inference when population has changed*, Communications in Statistics: Simulation and Computation, vol. 43(10), pp. 2570-2592, 2014: [web] [pdf].

- C. Bouveyron and C. Brunet, *Discriminative variable selection for clustering with the sparse Fisher-EM algorithm*, Computational Statistics, vol. 29(3-4), pp. 489-513, 2014: [web] [pdf].

- C. Bouveyron, *Probabilistic model-based discriminant analysis and clustering methods in Chemometrics*, Journal of Chemometrics, vol. 27(12), pp. 433-446, 2013: [web] [pdf].

- A. Bellas, C. Bouveyron, M. Cottrell & J. Lacaille, *Model-based clustering of high-dimensional data streams with online mixture of probabilistic PCA*, Advances in Data Analysis and Classification, vol. 7 (3), pp. 281-300, 2013: [web] [pdf].

- C. Bouveyron and C. Brunet, *Theoretical and practical considerations on the convergence properties of the Fisher-EM algorithm*, Journal of Multivariate Analysis, vol. 109, pp. 29-41, 2012: [web] [pdf].

- L. Bergé, C. Bouveyron and S. Girard, *HDclassif: an R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data*, Journal of Statistical Software, vol. 42 (6), pp. 1-29, 2012: [web] [pdf].

- C. Bouveyron and C. Brunet, * Probabilistic Fisher discriminant analysis: A robust and flexible alternative to Fisher discriminant analysis*, Neurocomputing, vol. 90 (1), pp. 12-22, 2012: [web] [pdf].

- C. Bouveyron and C. Brunet, *Simultaneous model-based clustering and visualization in the Fisher discriminative subspace*, Statistics and Computing, vol. 22 (1), pp. 301-324, 2012: [web] [pdf].

- C. Bouveyron and C. Brunet, *On the estimation of the latent discriminative subspace in the Fisher-EM algorithm*, Journal de la Société Française de Statistique, vol. 152 (3), pp. 98-115, 2011: [web] [pdf].

- C. Bouveyron, P. Gaubert and J. Jacques, *Adaptive models in regression for modeling and understanding evolving populations*, Journal of Case Studies in Business, Industry and Government Statistics, vol. 4 (2), pp. 83-92, 2011: [web] [pdf].

- C. Bouveyron, G. Celeux and S. Girard, *Intrinsic Dimension Estimation by Maximum Likelihood in Isotropic Probabilistic PCA*, Pattern Recognition Letters, vol. 32 (14), pp. 1706-1713, 2011: [web] [pdf].

- C.Bouveyron and J.Jacques, *Model-based Clustering of Time Series in Group-specific Functional Subspaces*, Advances in Data Analysis and Classification, vol. 5 (4), pp. 281-300, 2011: [web] [pdf].

- C. Bouveyron, O. Devos, L. Duponchel, S. Girard, J. Jacques & C. Ruckebusch, *Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data*, Journal of Chemometrics, vol. 24 (11-12), pp. 719-727, 2010: [web] [pdf].

- C. Bouveyron and J. Jacques, *Adaptive linear models for regression: improving prediction when population has changed*, Pattern Recognition Letters, vol. 31 (14), pp. 2237-2247, 2010: [web] [pdf].

- C. Bouveyron and S. Girard, *Robust supervised classification with mixture models: Learning from data with uncertain labels*, Pattern Recognition, vol. 42 (11), pp. 2649-2658, 2009 : [web] [pdf].

- C. Bouveyron and S. Girard, *Classification supervisée et non supervisée des données de grande dimension*, La revue Modulad, vol. 40, pp. 81-102, 2009 : [web] [pdf].

- C. Bouveyron, S. Girard and C. Schmid, *High-Dimensional Data Clustering*, Computational Statistics and Data Analysis, vol. 52 (1), pp. 502-519, 2007: [web] [pdf].

- C. Bouveyron, S. Girard and C. Schmid, *High Dimensional Discriminant Analysis*, Communications in Statistics: Theory and Methods, vol. 36 (14), pp. 2607-2623, 2007: [web].

- C. Bouveyron, S. Girard and C. Schmid, *Class-Specific Subspace Discriminant Analysis for High-Dimensional Data*, In Lecture Notes in Computer Science n°3940, pp. 139-150, Springer-Verlag, 2006: [web].

- C. Bouveyron *Apprentissage statistique en grande dimension et application au diagnostic oncologique par radiomique*, in C. Villani & B. Nordlinger, Santé et intelligence artificielle, CNRS Editions, pp. 179-189, 2018: [web].

- C. Bouveyron, C. Ducruet, P. Latouche and R. Zreik, *Cluster dynamics in the collapsing Soviet shipping network*, in Advances in Shipping Data Analysis and Modeling, Routledge, 2018: [web].

- C. Bouveyron, *Model-based clustering of high-dimensional data in Astrophysics*, in Statistics for Astrophysics: Clustering and Classification, EAS Publications Series, EDP Sciencs, vol. 77, pp. 91-119, 2016: [web] [pdf].

- C. Bouveyron, C. Ducruet, P. Latouche and R. Zreik, *Cluster Identification in Maritime Flows with Stochastic Methods*, in Maritime Networks: Spatial Structures and Time Dynamics, Routledge, 2015: [web].

- F. Beninel, C. Biernacki, C. Bouveyron, J. Jacques and A. Lourme, Parametric link models for knowledge transfer in statistical learning, in Knowledge Transfer: Practices, Types and Challenges, Ed. Dragan Ilic, Nova Publishers, 2012: [web].

- C. Bouveyron, G. Fouetillou, P. Latouche and D. Marié, *Elections 2017 : une réorganisation politique du web social ?*, The Conversation, juin 2017: [web].

- C. Bouveyron and P. Latouche, *Des réseaux, des textes et de la Statistique*, La lettre de l'INSMI, CNRS, December, 2016: [web].

- C. Bouveyron, *Apprentissage statistique en grande dimension : enjeux et avancées récentes*, Journal de la Société Française de Statistique, vol. 155 (2), pp. 36-37, 2014: [web].

- C. Bouveyron, *Discussion on the paper by J. Fan, Y. Liao and M. Mincheva*, Journal of the Royal Statistical Society, Serie B, 2013.

- C. Bouveyron, *Discussion on the paper by C. Hennig and T. Liao*, Journal of the Royal Statistical Society, Serie C, 2013.

- C. Bouveyron, S. Girard and F. Forbes, *Nouveaux défis en apprentissage statistique*, Journal de la Société Française de Statistique, vol. 152 (3), pp. 1-2, 2011: [web].

- C. Bouveyron, *Bayesian sparsity for statistical learning in high dimensions*, 51th Journées de Statistique de la SFdS, Nancy, France, June 2019.

- C. Bouveyron, *The Stochastic Topic Block Model*, President’s Invited Address, Annual Conference of The American Classification Society, New York, June 2018.

- C. Bouveyron, *Bayesian sparsity for statistical learning in high dimensions*, Chimiométrie 2018, Paris, January 2018.

- C. Bouveyron, *Model-based coclustering of functional data*, Annual Conference of the Italian Statistical Society, Florence, Italy, June 2017.

- C. Bouveyron, *Recent developments in model-based clustering of functional data*, 22nd International Conference on Computational Statistics, Oviedo, Spain, August 2016.

- C. Bouveyron, *Model-based clustering of functional data: application to the analysis of bike sharing systems*, 12th International Conference on Operation Research, Havana, Cuba, March 2016.

- C. Bouveyron, *Kernel discriminant analysis with parsimonious Gaussian process models*, 8th International Conference of the ERCIM WG on Computational and Methodological Statistics, London, UK, December 2015.

- C. Bouveyron, *Discriminative clustering of high-dimensional data*, Workshop on Model-Based
Clustering and Classification, Catania, Italy, September 2014.

- C. Bouveyron, *Discriminative variable selection for clustering*, 6th International Conference of the ERCIM, WG on Computational and Methodological Statistics, London, UK, December 2013.

- C. Bouveyron, *The random subgraph model for the analysis of an ecclesiastical network in merovingian Gaul*, 20th Summer Working Group on Model-Based Clustering of the Department of Statistics of the University of Washington, Bologna, Italy, July 2013.

- C. Bouveyron, *Clustering discriminatif et parcimonieux de données de grande dimension*, Conférence du prix Simon Régnier, 19th meeting of the Société Francophone de Classification, Marseille, 2012.

- C. Bouveyron, *Parsimonious and sparse Gaussian models for high-dimensional clustering*, International Classification Conference 2011, St Andrews, UK, July 2011.

- C. Bouveyron, *Model-based clustering of high-dimensional data: an overview and some recent advances*, 17th Summer Working Group on Model-Based Clustering of the Department of Statistics of the University of Washington, Grenoble, France, July 2010.

- C. Bouveyron, *Classification of complex data with model-based techniques*, 1st joint meeting of the Statistical Society of Canada and the Société Française de Statistique, Ottawa, Canada, 2008.

- C. Bouveyron, *An overview on high-dimensional data classification with model-based techniques*, 8th International Conference on Operations Research, Havana, Cuba, 2008.

I code to relax.

- Linkage.fr: this SaaS platform implements the STBM clustering technique for analyzing networks with textual edges.
The user can analyze with Linkage networks such as email networks or co-authorship networks. Linkage also allows users to upload their own
network data or to make requests on scientific databases such as Arxiv, Pubmed or HAL.

- FunLBM: The funLBM algorithm allows to simultaneously cluster the rows and the columns of a data matrix where
each entry of the matrix is a function or a time series. Available on the CRAN.

- SpinyReg: this package implements a generative model for Bayesian variable selection in high-dimensional linear regression. It uses a
spike-and-slab like prior distribution obtained by multiplying a deterministic binary vector with with a random Gaussian parameter vector. Such
a model allows the use of an EM algorithm, optimizing a type-II log-likelihood, for inference. Available on the CRAN.

- ProbFDA: this package proposes the probabilistic Fisher discriminant analysis technique for dimensionality reduction and classification.
The pFDA method works at least as well as the traditional FDA method in standard situations and it clearly improves the modeling and the prediction
when the dataset is subject to label noise and/or sparse labels. Available on the
CRAN.

- RobustDA: the package implements the robust mixture discriminant analysis (RMDA) which allows to build a robust supervised
classifier from learning data with label noise. Available on the CRAN.

- AdaptDA: this package provides the adaptive mixture discriminant analysis (AMDA) which allows to adapt a model-based classifier
to the situation where a class represented in the test set may have not been encountered earlier in the learning phase. Available on
the CRAN.

- FunFEM: the package implements the funFEM algorithm for the clustering of functional data within a disciminative functional subspace.
Available on the CRAN.

- Rambo: the package proposes the VB-EM algorithm for the random subgraph model (RSM) which allows to cluster the vertices of a directed
network with typed edges into clusters describing the connection patterns of subgraphs given as inputs. Available on
the CRAN.

- FisherEM: the package provides the FisherEM algorithm for the clustering of high-dimensional data. Available on
the CRAN.

- HDclassif: the package implements the HDDC and HDDA algorithms designed respectively for the clustering and classification
of high-dimensional data. Available on the CRAN.

- FunHDDC: the package implements the funHDDC algorithm which allows the clustering of functional data within group-specific
functional subspaces. Available on the CRAN.

- AdaptReg: the package provides tools for transfering a regression model on a reference population to a new population with
only few observations.

- LLN: the package provides tools for learning supervised classifiers on networks and classifying new arriving nodes in the
network.

- PgpDA the Python toolbox implements a classification method based on a family of parsimonious Gaussian process models.
This allows in particular to use non-linear mapping functions which project the observations into infinite dimensional spaces.

- HDDA/HDDC: the Python toolbox implements the HDDC and HDDA algorithms designed respectively for the clustering and
classification of high-dimensional data.

- HDDA/HDDC: the toolbox implements the HDDC and HDDA algorithms designed respectively for the clustering and
classification of high-dimensional data.

- Kde Image Menu (Kim): Kim is a KDE menu script which allows to convert, resize, rename and many other actions on a set of images.

I learn as much as I transmit.

Marco Corneli, Co-clustering of textual data matrices, Université Côte d'Azur, 2017-201975%

Fanny Orlhac, Joint statistical analysis of radiomic and metabolomic features to improve diagnosis and therapy in oncology, Inria Sophia-Antipolis, 2017-201975%

Warith Harchaoui, Model-based deep learning for mixed and complex data, Université Paris Descartes & Oscaro.com, 2016-201975%

Alexandre Saint-Dizier, Bayesian deep learning for image restauration, Université Paris Descartes & Ecole Polytechnique, 2017-202050%

Nicolas Jouvin, Statistical learning for image segmentation with a textual supervision: application to cancer detection, Université Paris 1 Panthéon-Sorbonne & Institut Curie, 2017-202050%

Laurent Bergé, Model-based clustering of communication networks, Université Paris Descartes, 2015-2016100%

Pierre-Alexandre Mattei, Model-based sparse clustering for massive and high-dimensional data, Université Paris Descartes, 2014-2017100%

Rawya Zreik, Statistical analysis of temporal networks and applications to historical sciences, , Université Paris Descartes, 2013-2016100%

Anastasios Bellas, Online anomaly detection in high-dimensional data streams, Université Paris 1 Panthéon-Sorbonne, 2011-2014100%

Camille Brunet, Sparse and discriminative clustering of high-dimensional data, Université d'Evry, 2008-2011100%

How to reach me.

Laboratoire J.A. Dieudonné

28 avenue Valrose

06108 Nice Cedex 02, France

+33 (0)4 92 07 61 62

+33 (0)4 89 73 24 51