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Research

EcoNet project

The webpage for the EcoNet project can be found here.

Research themes

• Higher order interactions modeled through hypergraphs.
• Ecological and biological networks: statistical approaches for infering, analysing and studying networks and their evolution.
• Models of co-evolution and co-phylogeny. Hosts-symbionts systems.
• Comparative genomics: sequence evolution, alignment.
• Multiple testing, semi-parametric modeling associated to this framework.
• Statistics for random walk in random environment.
• Multi-states semi-Markov models. Applications to modeling dependency of elderly people (G. Biessy PhD thesis).

Publications

Preprints

38. Claudia Delli Colli, Blerina Sinaimeri, Giuseppe F. Italiano, Igor Branchi & Catherine Matias. Psychometric network inference: A comparative analysis. Soumis, 2024, PsyArXiv preprint and the code to reproduce the analyses.

Publications in journals

37. Luca Brusa & Catherine Matias. Model-based clustering in simple hypergraphs through a stochastic blockmodel. Scandinavian Journal of Statistics, 2024. Journal link, HyperSBM R package and the code to reproduce the analyses.
38. Veronica Poda & Catherine Matias. Comparison of modularity-based approaches for nodes clustering in hypergraphs. Peer Community Journal, 4, article e37, 2024. Journal link. Code to reproduce the analyses.
39. Blerina Sinaimeri, Laura Urbini, Marie-France Sagot & Catherine Matias. Cophylogeny Reconstruction Allowing for Multiple Associations Through Approximate Bayesian Computation. Systematic Biology, syad058, 2023. Journal link, Hal preprint, AmoCoala software with datasets.
40. Marc Ohlmann, Giovanni Poggiato, Stéphane Dray, Wilfried Thuiller, Catherine Matias, Vincent Miele. Quantifying the overall effect of biotic interactions on species distributions along environmental gradients. Ecological Modelling, 483, 110424, 2023. Journal link, Hal preprint, the code for ELGRIN is part of the Econetwork R package.
41. Christophe Botella, Stéphane Dray, Catherine Matias, Vincent Miele & Wilfried Thuiller. An appraisal of graph embeddings for comparing trophic network architectures. Methods in Ecology and Evolution, 13(1), 203-216, 2022. Journal link, Hal preprint.
42. Suzana de Siqueira Santos, André Fujita & Catherine Matias. Spectral density of random graphs: convergence properties and application in model fitting. Journal of Complex Networks, 9(6), cnab041, 2021. Hal preprint, Journal link.
43. 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
44. 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, PPanGGOLiN Software.
45. 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.
46. Vincent Miele, Catherine Matias, Stéphane Robin & Stéphane Dray, Nine Quick Tips for Analyzing Network Data. Plos Computational Biology, 15(12):e1007434, 2019. Journal link.
47. 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. There is also a talk here.
48. 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.
49. 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.
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).

50. 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.
51. 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.
52. 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.
53. Mahendra Mariadassou & Catherine Matias, Groups posterior distribution in latent or stochastic block models for matrices. Bernoulli, 21(1):537-573, 2015. Journal link.
54. 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.
55. 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.
56. 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.
57. 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.
58. 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.
59. 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.
60. 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
61. 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.
62. 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.
63. Julien Chiquet, Alexander Smith, Gilles Grasseau, Catherine Matias & Christophe Ambroise, SIMoNe : Statistical Inference for MOdular NEtworks. Bioinformatics, 25(3): 417-418, 2009. Journal link, R package SIMoNe.
64. 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.
65. 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.
66. 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.
67. 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.
68. 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.
69. 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.
70. 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.
71. 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.
72. 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.
73. Catherine Matias, Semiparametric deconvolution with unknown noise variance. ESAIM Probab. & Stat., 6: 271-292, 2002. Journal link, pdf link.
74. 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)

• Catherine Matias, Statistique asymptotique dans des modèles à variables latentes. Habilitation à diriger des recherches, Université d'Évry val d'Essonne, 2008. pdf link
• Catherine Matias, Estimation dans des modèles à variables cachées, Thèse de doctorat (sous la direction d'Élisabeth Gassiat), Université Paris XI- Orsay, 2001. pdf link

(Some) Conference Slides

• Half-day autumn school on hypergraphs organized by Bernoulli Society Committee on Statistical Network Science (online). Statistical modeling of higher-order interactions - towards node clustering. pdf link and also recording on this page
• Workshop on Statistical Analysis of Networks, (Univ of Warwick). Model-based clustering in simple hypergraphs through a stochastic blockmodel. pdf link
• Mathematics of disordered systems: a tribute to Francis Comets (Paris). Statistical success of MAMAs : a guided tour through Francis' contributions. pdf link
• Mini-symposium ``Simulation de séquences biologiques et de processus évolutifs'' en marge de Jobim 2022. (Paris). Cophylogeny reconstruction in host-symbionts systems via Approximate Bayesian Computation and allowing for multiple associations. pdf link

PhD and Post-Doc Students

Present

• Rémi Boutin (Sorbonne Université, projet Emergence). Post-doctorant (13 mois). Sujet : Sparse hypergraphs modeling.
• Lucie Arts (Sorbonne Université). Doctorante. Sujet : Model selection in SBM.

Past

• Luca Brusa (Università di Milano Bicocca, Italia). Visting phD student under the supervision of Fulvia Pennoni and Francesco Bartolucci.
• Christophe Botella (CNRS, ANR EcoNet). Post-doctorant (19 mois). Sujet : Spatial analysis of ecological networks.
• Léa Longepierre (UPMC). Doctorante. Estimation par maximum de vraisemblance dans des modèles à blocs stochastiques dynamiques ou spatiaux. Soutenue le 2 juillet 2020. pdf link.
• Suzana de Siqueira Santos (USP, São Paulo). Visting phD student under the supervision of André Fujita.
• Laura Urbini (Univ Lyon 1). Doctorante co-encadrée avec Marie-France Sagot. Sujet : Modèles et algorithmes pour étudier les interactions symbiotiques. Soutenue le 23 Octobre 2017. pdf link.
• Guillaume Biessy (SCOR, UEVE). Doctorant. Sujet : Modélisation du risque de dépendance des personnes âgées. Financement CIFRE avec la SCOR. Thèse de l'Université d'Évry val d'Essonne - Paris Saclay. Soutenue le 28 novembre 2016. pdf link.
• Morgane Pierre-Jean (UEVE). Doctorante. Cette thèse a été entièrement encadrée par : Pierre Neuvial. Sujet : Méthodes statistiques pour l'analyse de données génomiques structurées le long du génome; applications en cancérologie. Thèse de l'Université d'Évry val d'Essonne - Paris Saclay. Soutenue le 02 décembre 2016.
• Van Hanh Nguyen (Univ. Paris Sud-Orsay). Doctorant. Sujet : Modèles de mélange semi-paramétriques et applications aux tests multiples. Thèse de l'Université Paris XI- Orsay. Soutenue le 1er octobre 2013. pdf link.
• Audrey Finkler (Univ. Strasbourg). Doctorante co-encadrée avec Leonid Galtchouk. Sujet : Modèle d'évolution avec dépendance au contexte et Corrections de statistiques d'adéquation en présence de zéros aléatoires. Thèse de l'Université de Strasbourg. Soutenue en juin 2010, co-encadrement avec L. Galtchouk. pdf link.

Teaching

Present

• Mini-course on Statistical Analysis of Graphs - Universidade de São Paulo - October 2023. Recordings of the lectures.

Past

• Formation via CNRS Formation Entreprises : Introduction au machine learning et au deep learning avec Python. Plus d'infos ici.
• Formation via CNRS Formation Entreprises : Analyse statistique des réseaux. Plus d'infos ici.
  
• Avant 2018: Page du cours Analyse statistique de graphes. Cours du Master (M2) Mathématiques et Applications, Spécialité Statistique de l'UMPC.
• Analysis of Biological Sequences, October 2014, LNCC, Petrópolis, Brasil. pdf.
• Page du cours : Introduction à la statistique pour la biologie moléculaire, dispensé à l'Ensae.
• Cours de Statistique non paramétrique, dispensé à l'Ensiie. Contenu : Introduction ; Fonctions de répartition et fonctionnelles de la distribution ; Tests non paramétriques ; Estimation de densité ; Régression non paramétrique ; Ré-échantillonnage.
• Atelier SFDS : Introduction à la statistique non paramétrique, les 27-28 septembre 2012, à l'IHP. Supports : Intro & cdf, tests, estimation de densités 1ère partie et 2nde partie.
• Cours de génomique comparative (dans le cadre d'un atelier statistique de la SFDS, formation en 3h). pdf link .

Responsibilities

• 2020-24: Membre nommée de la Commission Scientifique Spécialisée (CSS) MISTI (mathématiques, informatique, sciences et technologies du numérique, intelligence artificielle et robotique) INRAE. Également membre du bureau.
• 2023-: Membre extérieure de externe de l'École Doctorale Évolution, écosystèmes, microbiologie, modélisation (ED E2M2), Université de Lyon.
• 2018-21: Directrice adjointe scientifique de l'INSMI
• 2015-17: Membre nommé de la Commission Nationale des Universités (CNU, Section 26). Membre du bureau élargi.
• 2016-17: Associate editor for International Journal of Biostatistics.
• 2015-17: Associate editor for Statistics and Probability letters.

Others links/info

Seminars

• Séminaire Parisien de Statistiques
• Séminaire de Statistiques du LPSM

Climbing

• Bleau.info
• Cosiroc (topos des grands avaux)
• Camp to Camp
• Agrippine