My office

Sorbonne Université - Campus Pierre et Marie Curie
Tours 15-16, Bureau 212 (2ème étage)


Sorbonne Université - Campus Pierre et Marie Curie
Boite courrier 158
4 place Jussieu
75252 PARIS Cedex 05, FRANCE

Research contact

NOTE: Starting January 2018, I am on leave as a scientific deputy director at INSMI. Merci de ne pas utiliser les mail/téléphone ci-dessus pour me joindre au sujet de l'INSMI. Veuillez utiliser les informations de cette page.
2019 - 2020 : c'est l'année des Mathématiques ! Plus d'infos ici.


EcoNet project

The webpage for the EcoNet project can be found here.

Research themes

NEW: Appel de Jussieu pour la Science ouverte et la bibliodiversité // Jussieu Call for Open science and bibliodiversity


  1. Suzana de Siqueira Santos, André Fujita & Catherine Matias, Spectral density of random graphs: convergence properties and application in model fitting. Soumis, 2021 Hal preprint.

Publications in journals
  1. Estelle Kuhn, Catherine Matias & Tabea Rebafka, Properties of the Stochastic Approximation EM Algorithm with Mini-batch Sampling, Statistics and Computing, 30, 1725-1739, 2020. Journal link, Hal preprint
  2. Gautreau, Bazin, Gachet, Planel, Burlot, Dubois, Perrin, Médigue, Calteau, Cruveiller, Matias, Ambroise, Rocha, Vallenet, PPanGGOLiN: depicting microbial species diversity via a Partitioned Pangenome Graph, Plos Computational Biology, 16(3): e1007732, 2020. Journal link, Software.
  3. Léa Longepierre & Catherine Matias, Consistency of the maximum likelihood and variational estimators in a dynamic stochastic block model. Electronic Journal of Statistics, 13(2):4157-4223, 2019. Journal link, HAL preprint.
  4. Vincent Miele, Catherine Matias, Stéphane Robin & Stéphane Dray, Nine Quick Tips for Analyzing Network Data. Plos Computational Biology, 15(12):e1007434, 2019. Journal link.
  5. Catherine Matias, Tabea Rebafka & Fanny Villers, A semiparametric extension of the stochastic block model for longitudinal networks. Biometrika, 105(3): 665-680, 2018. Journal link, Take a look at the Supplementary material (contains an important generalisation of the model), HAL preprint, R code with datasets analyses, R package ppsbm.
  6. Laura Urbini, Blerina Sinaimeri, Catherine Matias & Marie-France Sagot, Exploring the Robustness of the Parsimonious Reconciliation Method in Host-Symbiont Cophylogeny. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(3), 738-748, 2019. Journal link, Hal preprint.
    NB: This is an extended version of that conference paper.
  7. Ana Arribas-Gil & Catherine Matias, A time warping approach to multiple sequence alignment. Statistical Applications in Genetics and Molecular Biology, 16(2): 133-144, 2017. Journal link, Hal preprint. Codes available on request.
  8. 25bis.   Vincent Miele & Catherine Matias, Revealing the hidden structure of dynamic ecological networks. Royal Society Open Science, 4:170251, 2017. Journal link. NB: This is a popularization version of the previous work (Ref 25).
  9. Catherine Matias & Vincent Miele, Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B, 79(4): 1119-1141, 2017. Journal link , HAL preprint, R package dynSBM. See also the erratum.
  10. Pierre Andreoletti, Dasha Loukianova & Catherine Matias, Parametric estimation of a one-dimensional ballistic random walk in a Markov environment. ESAIM Prob. & Stat.19: 605-625, 2015. pdf link, Journal link.
  11. C. Baudet, B. Donati, B. Sinaimeri, P. Crescenzi, C. Gautier, C. Matias & M-F. Sagot, Co-phylogeny reconstruction via an approximate Bayesian computation. Systematic Biology. 64(3): 416-431, 2015. Journal link, Coala Software.
  12. Mahendra Mariadassou & Catherine Matias, Groups posterior distribution in latent or stochastic block models for matrices. Bernoulli, 21(1):537-573, 2015. Journal link.
  13. Catherine Matias & Stéphane Robin, Modeling heterogeneity in random graphs through latent space models: a selective review. Esaim Proc. & Surveys, 47: 55-74, 2014. pdf link, Journal link.
  14. Mikael Falconnet, Dasha Loukianova & Catherine Matias, Asymptotic normality and efficiency of the maximum likelihood estimator for the parameter of a ballistic random walk in a random environment. Mathematical Methods of Statistics. 23(1) :1-19, 2014. pdf link, Journal link.
  15. Francis Comets, Mikael Falconnet, Oleg Loukianov, Dasha Loukianova & Catherine Matias, Maximum likelihood estimator consistency for ballistic random walk in a parametric random environment. Stochastic Processes & Applications. 124(1) : 268-288, 2014. pdf link, Journal link.
  16. Van Hanh Nguyen & Catherine Matias, On efficient estimators of the proportion of true null hypotheses in a multiple testing setup. Scandinavian Journal of Statistics, 41(4): 1167-1194, 2014. pdf link, Journal link.
  17. Van Hanh Nguyen & Catherine Matias, Nonparametric estimation of the density of the alternative hypothesis in a multiple testing setup. Application to local false discovery rate estimation. ESAIM Prob. & Stat.. 18: 584-612, 2014. pdf link, Journal link.
  18. Ana Arribas-Gil & Catherine Matias, A context dependent pair hidden Markov model for statistical alignment. Statistical Applications in Genetics and Molecular Biology, 11(1), Article 5, 2012. pdf link, Journal link.
  19. Christophe Ambroise & Catherine Matias, New consistent and asymptotically normal parameter estimates for random graph mixture models. Journal of the Royal Statistical Society: Series B, 74(1): 3-35, 2012. pdf link , Journal link
  20. Elizabeth Allman, Catherine Matias & John Rhodes, Parameters identifiability in a class of random graph mixture models. Journal of Statistical Planning and Inference, 141: 1719-1736, 2011. pdf link, Journal link.
  21. Elizabeth Allman, Catherine Matias & John Rhodes, Identifiability of parameters in latent structure models with many observed variables. Annals of Statistics, 37(6A): 3099-3132, 2009. Journal link.
  22. Julien Chiquet, Alexander Smith, Gilles Grasseau, Catherine Matias & Christophe Ambroise, SIMoNe : Statistical Inference for MOdular NEtworks. Bioinformatics, 25(3): 417-418, 2009. Journal link, SIMoNe R package.
  23. Christophe Ambroise, Julien Chiquet & Catherine Matias, Penalized maximum likelihood inference for sparse Gaussian graphical models with hidden structure. Electronic Journal of Statistics, 3: 205-238, 2009. Journal link.
  24. Antoine Chambaz & Catherine Matias, Number of hidden states and memory: a joint order estimation problem for Markov chains with Markov regime. ESAIM Probab. & Stat., 13: 38-50, 2009. Journal link.
  25. Cristina Butucea, Catherine Matias & Christophe Pouet, Adaptive goodness-of-fit testing from indirect observations. Annales de l'Institut Henri Poincaré, 45(2): 352-372, 2009. Journal link.
  26. Cristina Butucea, Catherine Matias & Christophe Pouet, Adaptivity in convolution models with partially known noise distribution. Electronic Journal of Statistics, 2: 897-915, 2008. Journal link.
  27. Ana Arribas-Gil, Élisabeth Gassiat & Catherine Matias, Parameter estimation in pair hidden Markov models. Scandinavian Journal of Statistics, 33(4): 651-671, 2006. pdf link, Journal link.
  28. Catherine Matias, Sophie Schbath, Étienne Birmelé, Jean-Jacques Daudin & Stéphane Robin, Networks motifs : mean and variance for the count. Revstat, 4(1): 31-51, 2006. Journal link.
  29. Ismaël Castillo, Céline Lévy-Leduc & Catherine Matias, Exact adaptive estimation of the shape of a periodic function with unknown period corrupted by white noise. Mathematical Methods of Statistics, 15(2): 146-175, 2006. pdf link.
  30. Cristina Butucea & Catherine Matias, Minimax estimation of the noise level and of the deconvolution density in a semiparametric convolution model. Bernoulli, 11(2): 309-340, 2005. Journal link.
  31. Catherine Matias & Marie-Luce Taupin, Minimax estimation of linear functionals in the convolution model. Mathematical Methods of Statistics, 13(3): 282-328, 2004. pdf link.
  32. Catherine Matias, Semiparametric deconvolution with unknown noise variance. ESAIM Probab. & Stat., 6: 271-292, 2002. Journal link
  33. Randal Douc & Catherine Matias, Asymptotics of the maximum likelihood estimator for general hidden Markov models. Bernoulli, 7(3): 381-420, 2001. Journal link.

Unpublished manuscripts (in french)

(Some) Conference Slides

PhD and Post-Doc Students




Formation via CNRS Formation Entreprises : Analyse statistique des réseaux.
Cette année, la formation aura lieu du 28 au 30 novembre 2018. Accessible sur inscription. Plus d'infos ici.



Others links/info


Journals / Bibliography

Others (maths related)