Invited speakers

Gérard Biau is a Professor at Sorbonne University and Director of SCAI, the university’s research center in artificial intelligence. He is recognized for his work on the theoretical foundations of machine learning, in particular on random forests, boosting, and neural networks. He is a member of the Institut Universitaire de France and was elected to the French Academy of Sciences in 2024.

Adeline Fermanian is a researcher in statistics and machine learning, currently Head of Research at Califrais, where she develops optimization methods for sustainable food logistics. Her work focuses on time series analysis through signatures, neural differential equations, and combinatorial optimization. She is the co-author of several publications in NeurIPS, JMLR, and Computational Statistics & Data Analysis.

Sarah Filippi is a Professor in Statistical Machine Learning in the Department of Mathematics at Imperial College London, and Co-Director of the EPSRC Centre for Doctoral Training in Statistics and Machine Learning (StatML), in partnership with Oxford. Her research focuses on computational statistics and machine learning, with applications in computational biology and biomedical genetics.

Eva Löcherbach is a Professor of Mathematics at École Polytechnique and a member of CMAP, specializing in probability and statistics of stochastic processes. Her research focuses on limit theorems for recurrent Markov processes, interacting particle systems (Hawkes processes, variable memory chains), and probabilistic models inspired by neuroscience. She has also contributed to statistical physics, particularly through the study of propagation of chaos and hydrodynamic limits.

Adrian E. Raftery is Emeritus Professor at the University of Washington, recognized for his major contributions to Bayesian statistics, demographic modeling, and model selection. Thomson-ISI identified him as the world’s most cited researcher in mathematics for the period 1995–2005. He is a member of the U.S. National Academy of Sciences and of several international scholarly societies.

Andrea Rau is a Research Director in statistics and genomics at INRAE, within the GABI unit (Animal Genetics and Integrative Biology) in Jouy-en-Josas. Her research focuses on the statistical analysis of high-throughput sequencing data (RNA-seq), mixture models, Bayesian approaches, and causal inference in gene networks.

Tabea Rebafka is a Professor at AgroParisTech and an affiliated researcher at LPSM, Sorbonne University. Her research focuses on statistical modeling of networks, mixture models, nonparametric inference, and stochastic EM algorithms. She has also contributed to methods for controlling the false clustering rate in mixture models and to the analysis of partially observed data.

Vincent Rivoirard is a Professor at Université Paris Dauphine–PSL and a member of the CEREMADE laboratory. His research focuses on nonparametric and high-dimensional statistics, using both Bayesian and frequentist approaches. He is particularly interested in applications to neuroscience, genetics, and biology. He served as Director of CEREMADE from 2016 to 2022.

Pierre Tandeo is an Associate Professor at IMT Atlantique (Brest campus) and a researcher at Lab-STICC (CNRS). His research focuses on data assimilation, machine learning, and geophysical modeling. He is also an associate researcher at the RIKEN Center for Computational Science (Japan), within the Data Assimilation team. He is co-author of innovative methods combining neural networks and data assimilation for dynamical systems.

Nicolas Vayatis is a Professor, Dean of the Department of Mathematics, and Director of the Borelli Center at École Normale Supérieure Paris-Saclay, where he leads research in machine learning and large-scale data analysis. His expertise includes high-dimensional statistics, predictive modeling, networks, and uncertainty, with applications in healthcare, industry, and transportation.