The 11th Rencontres de Statistique welcome ten speakers.

Antoine Barbieri — Bordeaux Population Health, Inserm U1219, Univ. de Bordeaux

Antoine Barbieri carries out his research in the Biostatistics team at Bordeaux Population Health. His work focuses mainly on distributional regression models with latent variables and on survival models. More recently, he has turned to the modeling of heterogeneous longitudinal data, with the aim of better explaining and predicting the risk of cardiovascular and cerebrovascular events.

Lise Bellanger-Husi — Lab-STICC, Univ. Bretagne Sud

Lise Bellanger-Husi's work focuses on the statistical analysis of movement and gait using sensor data. Developed in collaboration with clinical and industrial teams, these methods aim to characterize and monitor gait disorders in neurodegenerative diseases, notably multiple sclerosis, using measurements taken in patients' daily lives rather than only at the time of the consultation.

Félix Cheysson — LAMA, CNRS UMR 8050, Univ. Gustave Eiffel

Félix Cheysson works on self-exciting point processes, and in particular on Hawkes processes. He has developed spectral estimation methods that make it possible to infer these processes from aggregated discrete-time data, a common situation in epidemiological surveillance where only periodic counts are available. His recent work focuses on Hawkes processes in a random environment.

Christian Dina — L'institut du thorax, Inserm UMR 1087 / CNRS UMR 6291, Nantes Université

Christian Dina carries out his research in the l'institut du thorax unit, dedicated to the genetics and pathophysiology of cardiac, vascular, metabolic and respiratory diseases. His work falls within statistical genetics and the analysis of high-throughput genomic data.

Valérie Garès — IRMAR, Inria Rennes

Valérie Garès's research focuses on the development of statistical methods for the analysis of health data. Her work addresses in particular the integration of heterogeneous data sources, especially the harmonization of variables, the linkage of databases, notably using SNDS data, and the transportability of causal relationships across different populations. She is also interested in functional data analysis, notably for the exploitation of mass spectrometry data

Yiye Jiang — IRMAR, Univ. de Rennes

Yiye Jiang's research lies at the interface of time series analysis, complex data analysis and graph learning, where she develops new statistical models suited to continuously evolving data. Her work covers both frequentist and Bayesian approaches, from methodology to computation and applications. She is particularly interested in the inference of brain functional connectivity networks.

Vera Kimmerling — Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Villejuif

Vera Kimmerling is preparing a PhD at the Centre for Research in Epidemiology and Population Health. Her work focuses on the impact of diagnostic errors (misclassification and diagnostic delays, which are common in large medico-administrative databases) on the estimation of the association between an exposure and disease risk. It combines an application to Parkinson's disease using the UK Biobank cohort with a simulation study based on a multi-state model with parametric survival functions. 

Nathalie Krell — IRMAR, Univ. Rennes 2

Nathalie Krell's work focuses on stochastic processes, in particular branching processes and piecewise-deterministic Markov processes. These models describe populations of individuals that grow, divide and pass on their characteristics, with applications to cell division and population dynamics.

Timothée Loranchet — Institut Pierre Louis d'épidémiologie et de santé publique, Inserm UMR-S 1136, Sorbonne Université

Timothée Loranchet is preparing a PhD at the Institut Pierre Louis d'épidémiologie et de santé publique (Pierre Louis Institute of Epidemiology and Public Health), one of whose teams is dedicated to causal inference in public health from large observational databases. His work focuses on methods for estimating treatment effects when the data do not come from a randomized trial.

Aymeric Stamm — Laboratoire de mathématiques Jean Leray, CNRS, Nantes Université

Aymeric Stamm develops statistical methods for complex data from the medical field, notably functional data and imaging. His projects rely on clinical collaborations, in particular with the Nantes University Hospital, and on work devoted to multiple sclerosis supported by the ARSEP Foundation.