Invited speakers

Gérard Biau is a Professor at Sorbonne University (LPSM) and Director of the Sorbonne Center for Artificial Intelligence (SCAI). A specialist in statistics and machine learning, he served as President of the French Statistical Society (2015–2018), is a former member of the Institut Universitaire de France, and was elected to the French Academy of Sciences in 2024.

Adeline Fermanian is Head of Research at Califrais in Paris, leading projects at the intersection of machine learning, logistics optimization, and sustainability to decarbonize the food supply chain. She holds a PhD in Statistics from Sorbonne University (2021) and completed a postdoc at the Center for Computational Biology at Mines ParisTech.

Sarah Filippi is a Professor in Statistical Machine Learning at Imperial College London. Her research focuses on Bayesian methods for modeling biological and medical data, with an emphasis on uncertainty, causality, and interpretability. She co-directs the EPSRC Centre for Doctoral Training in Statistics and Machine Learning.

Adrian E. Raftery is Blumstein–Jordan Professor Emeritus of Statistics and Sociology at the University of Washington, where he founded the Center for Statistics and the Social Sciences. He is known for his work on Bayesian model selection, model averaging, and probabilistic forecasting in demography, climate science, and epidemiology. He is a member of the U.S. National Academy of Sciences.

Andrea Rau is a Research Director in statistics and genomics at INRAE (Jouy‑en‑Josas, France), where she leads methods development for high‑throughput and multi‑omic data integration using R/Bioconductor tools. She earned her PhD in Statistics from Purdue University in 2010, focusing on gene regulatory networks, and has since developed open‑source software to advance genomic data analysis.

Tabea Rebafka is a Professor in machine learning and statistics at AgroParisTech (Institut Polytechnique de Paris) and Deputy Director of the LPSM at Sorbonne University. Her research focuses on advanced modeling for complex data, including network analysis, multiple testing, and nonparametric estimation, with recent contributions on graph clustering and stochastic mini-batch algorithms.

Vincent Rivoirard is a Professor at Université Paris Dauphine–PSL since 2010, specializing in nonparametric and high-dimensional statistics, with both Bayesian and frequentist approaches. He served as Director of the Ceremade research lab from 2016 to 2022. His work includes applications in neuroscience, genetics, and biology.

Pierre Tandeo is a Full Professor at IMT Atlantique (Brest, France) and a researcher with the CNRS lab STICC, also affiliated with RIKEN in Japan. A specialist in data assimilation, remote sensing, and uncertainty quantification, he develops machine-learning methods for oceanography and climate science. He holds an HDR from UBO and leads the “Ocean Data Science” master’s program.

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.