Research interests

Development of probabilistic models and statistical learning 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,

• Other Application fields : neuroscience, pharmacokinetics, food risk, EDF reliability, social networks