Chargée de Recherches  / Researcher at INRA 

UMR MIA-Paris, AgroParisTech, INRA, Université Paris-Saclay, 75005, Paris, France

Tel : +33 (0)1 44 08 37 19

sophie.donnet@inra.fr

Fields of Research

Development of probabilistic models and inference methods for ecology and life sciences.

• Models for complex interaction network data: multi-level, multiplex, multipartite ...
• Models defined by ordinary and stochastic differential equations.
• Bayesian statistics: elicitation of a priori distribution and combination of expert 
• Bayesian parametric and nonparametric statistics for counting processes.
• Statistical Inference for Gaussian Graphical Models
• Stochastic algorithms: MCMC, EM and stochastic versions, particule filters ...
• Applications in ecology: interaction networks pollinating plants, insects,
• Applications: neuroscience, pharmacokinetics, food risk, EDF reliability, social networks

Positions  

• Since sept. 2013 :  Researcher at INRA, Unité MIA Paris
• 2012-2014 : Long mission to Mexico, visiting researcher. CIMAT (Centro de Investigacion en Matematicas) et Instituto de Matematicas, Universidad Nacional Autonoma de México (UNAM).  
• 2007 - 2013 : Lecturer.  Université Paris-Dauphine. Membre du CEREMADE

Cursus universitaire

• 2018 : Habilitation à diriger les recherches 
  HDRpdf - 2,5 MB
• 2003 - 2006 : Doctorat de Statistiques. Thèse soutenue le 30/11/2006 à  l'Université Paris-Sud, préparée sous la direction de M. Lavielle (Université Paris-Descartes) et J.-B. Poline (INRIA-Neuropsin). Titre : ``Inversion de données IRMf. Estimation et sélection de modèles''. 
• 2002 - 2003 : DEA de Modélisation Stochastique et Statistique. Université Paris Sud. 
• 2001 - 2002 : Agrégation de mathématiques, option probabilités et statistique. 

Supervisions

• PhD  : since sept 2018 :  Saint-Clair Chabert-Liddell
• Master 2 : Souhila Founas (2019), Saint-Clair Chabert-Liddell (2018), Daouda Ba (2018), Timothée Tabouy (2016), Abdel Wahab Sow (2015), Marco Banterle et Jacopo Primavera (2012) 
• Master 1 : Thomas Vrignaud (2019), Jocelyn Vanhonacker (2018), Wei Huang (2010)}

Publications 

Articles submitted in international journals

• [S1]  Bayesian inference for network Poisson models, S. Donnet &  S. Robin  arxiv
• [S3]  Block models for multipartite networks.Applications in ecology and  ethnobiology. A. Bar-Hen, P. Barbillon & S. Donnet
• [S3]  A Stochastic Block Model for Multilevel Networks: Application to the Sociology of Organisations. Saint-Clair Chabert-Liddell, Pierre Barbillon, Sophie Donnet & Emmanuel Lazega arxiv

Published or accepted articles

• [WP1]  Using deterministic approximations to accelerate SMC for posterior sampling. S. Donnet & S. Robin.  arxiv
• [A17] S. Donnet, V. Rivoirard & J. Rousseau. Nonparametric Bayesian estimation of multivariate Hawkes processes.  Accepted for publication in Annals of Statistics arxiv
• [A16] E. Lazega, A. Bar-Hen, P. Barbillon, S. Donnet. Effects of competition on collective learning in advice networks Social Networks, Volume 47, October 2016, Pages 1–14 
  
  A16pdf - 1,5 MB
• [A15]  P. Barbillon, S. Donnet, E. Lazega, A. Bar-Hen. Stochastic block models for multiplex networks: an application ot multilevel network of researchers. JRSSA (2016)
  
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• [A14]  S. Donnet, V. Rivoirard, J. Rousseau, C. Scricciolo.  Posterior concentration rates for empirical Bayes procedures with applications to Dirichlet process mixtures. Bernoulli Journal (2016) 
• [A13] S. Donnet, V. Rivoirard, J. Rousseau, C. Scricciolo. Posterior concentration rates for counting processes with Aalen multiplicative intensities. Bayesian Analysis (2016) 
• [A12] M. Capistran, A. Christen, S. Donnet. Bayesian Analysis of ODE’s : solver optimal accuracy and Bayes factors. Journal of Uncertainty Quantification, 4, 829-849. 
  
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•  [A11] S. Donnet, J. Rousseau, J. Bayesian Inference for Partially Observed Branching Processes. Bayesian Analysis Volume 11, Number 1 (2016), 151-190
•  [A10] S. Donnet, Bartolo, R., Fernandes J.M., Cunha J.P., Prado, L. and Merchant, H. Monkeys time their movement pauses and not their movement kinematics during a synchronization-continuation rhythmic task Journal Of Neurophysiology, May 2014 ; 111(10), 2138
  
  A10pdf - 1,2 MB
•  [A9] S. Donnet, A. Samson. Using PMCMC in EM algorithm for stochastic mixed models : theoretical and practical issues Journal de la Société Françcaise de Statistique, 155, 49-72, 2014.
• [A8] S. Donnet, A. Samson. A review on estimation of stochastic differential equations for pharmacokinetic/ pharmacodynamic models. Advanced Drug Delivery Reviews
  
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• [A7] I. Albert, S. Donnet, C. Guihenneuc, S. Low-Choy, K. Mengersen, J. Rousseau, Combining expert opinions in prior elicitation (with discussion). Bayesian Analysis, 7(3), 503-546 (2012)
• [A6] S. Donnet, J.-M. Marin . An empirical Bayes procedure for the selection of Gaussian graphical models. Statistics and Computing, 22(5), 1113-1123 (2012)
  
  A06pdf - 1 MB
• [A5] S. Donnet , J-L Foulley, A. Samson. Bayesian analysis of growth curves using mixed models defined by stochastic differential equations . Biometrics, 66(3) :733-741, (2010)
  
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• [A4] P. Ciuciu, T. Vincent, L. Risser, S. Donnet. A joint detection-estimation framework for analysing within-subject fMRI data. Journal de la Société Franéaise de Statistiques, Vol. 151, No 1 (2010)
• [A3] S. Donnet, A. Samson. Parametric inference for mixed models defined by stochastic differential equations. ESAIM P&S, 12 :196-218, (2008)
  
  A03pdf - 325,6 kB
• [A2] S. Donnet, A. Samson. Estimation of parameters in missing data models defined by differential equations. J. Statist. Plann. Inference 137 (2007), no. 9, 2815–2831
  
  A02pdf - 267,1 kB
• [A1] S. Donnet, M. Lavielle, and J.-B. Poline. Are fMRI event related reponses constant across events ?. Neuroimage,Volume 31, Issue 3, 1 (July 2006), 1169 – 1176

Revues de livre et discussions

• [B3] S. Donnet. Book review of “Stochastic Modelling for Systems Biology (second edition)” by Darren J. Wilkinson. CHANCE 25-4 (Décembre 2012)
• [B2] S. Donnet. Book review of “Statistical Thinking in Epidemiology” by Yu-Kang Tu and Mark S. Gilthorpe. CHANCE 25-4 (Décembre 2012)
• [B1] S. Donnet. Book review of “Monte Carlo Simulation for the Pharmaceutical Industry : Concepts, Algorithms, and Case Studies” by Mark Chang. International Statistical Review (Avril 2012)
• [D1] S. Donnet, A. Samson. Discussion on “Parameter estimation for differential equations : a generalized smoothing approaché” (by Ramsay JO, Hooker G, Campbell D and Cao J), Journal of the Royal Statistical Society : Series B, 69(5) :741-796, (2007)