Judith Abécassis is a research scientist (ISFP) in the Soda team at Inria Saclay, where she bridges statistical methods in causal inference with medical applications to deliver actionable insights that enhance patient care. Collaborating closely with medical experts, she analyzes real -world observational data from the AP-HP Clinical Data Warehouse - focusing on diabetes, inflammatory diseases, and strokes - as well as administrative claims data. Her work aims to translate complex data into evidence-based recommendations for clinical decision-making. She holds a PhD in Bioinformatics from the Center for Computational Biology at Mines ParisTech and the RT2 Lab (Tumor Residue and Treatment Response) at Institut Curie, where she specialized in high-throughput sequencing data analysis from cancer genomes.

Augusto Anguita-Ruiz is a Junior Research Leader at the Barcelona Institute for Global Health (ISGlobal) and an Adjunct Professor at the Faculty of Pharmacy, University of Barcelona. He holds a PhD in Nutritional Biochemistry from the University of Granada. His research focuses on the integration of exposome and multi-omics data to understand the early-life origins of obesity, insulin resistance, and metabolic dysfunction. He applies explainable artificial intelligence (XAI) and systems biology approaches to characterize molecular trajectories and identify predictive biomarkers across the life course. He has published over 40 peer-reviewed articles and participated in large-scale European projects such as ATHLETE and IHEN. His recent work includes the coordination of EXOMAIR, a national project combining exposome profiling with AI to inform precision prevention strategies in pediatric populations.

Marc Chadeau is a Professor of Computational Epidemiology & Biostatistics in the Department of Epidemiology and Biostatistics at the School of Public Health. His main research interests resides in the application of novel statistical approaches to address biologically and epidemiologically driven questions. His work primarily focuses on: 1)Computationally efficient models for profiling and integrating data from high-throughput platforms and 2)Dynamic models for disease progression, aimed at identifying key features driving disease dynamics and progression. At the confluence of these two research streams, his current research focuses on the analysis and integration of OMICs markers in relation to complex exposures and or health outcomes. This work aims at characterising molecular signatures of the exposome (including environmental exposures and social factors) and to explore the mechanisms involved in the mediation of these effects.

Sébastien Déjean is a Research Engineer at the Institute of Mathematics of Toulouse, and an expert in statistical information. For several years, he has been actively engaged in interdisciplinary research projects that integrate statistics - particularly data analysis - with other scientific domains. His collaborations in the field of biology are primarily oriented towards the analysis and integration of omics data.

Quentin Dufour is a Junior Professor at the Centre Norbert Elias and Deputy Director of the The Institut Santé Numérique en Société (PariSanté Campus). His research project focuses on the political and ethical issues involved in the reuse of health data - a practice widely endorsed by public authorities since the late 2000s. Situated at the intersection of Science and Technology Studies (STS) and Data Studies, it examines work situations in which groups of actors intervene to make data reusable. The central hypothesis is that this intervention is not purely technical. It entails decisions about the nature of the data, the kinds of knowledge they produce, the uses to which they can be put, and the forms of regulation they may - or may not- comply with. In other words, the ethical and political dimensions of reuse are embedded in the data themselves. The research adopts an ethnographic approach to follow how these issues materialize in data work, what tensions they generate, and how such tensions are addressed in practice. Empirically, the project focuses on reuse cases across different domains: epidemiological cohorts, genomics, and AI models applied to health.

David Hajage is a University Professor and Hospital Practitioner in Biostatistics within the Department of Public Health at Pitié-Salpêtrière Hospital. He is a member of the PEPITES team (Pharmaco-epidemiology and Healthcare Assessment) at the Pierre Louis Institute of Epidemiology and Public Health. The team’s research focuses on high-risk populations with specific characteristics (e.g., elderly or intensive care patients), benefit–risk assessment of biologics across various indications, and healthcare evaluation, with particular attention to the underlying processes of patient management (e.g., admission, hospital stay, discharge procedures) and their impact on drug benefit–risk profiles. Their work relies on the use of field data, healthcare administrative databases, and in-hospital clinical data warehouses. It involves the use or development of a variety of methodological approaches, often in combination - such as patient-reported outcomes and e-cohorts to incorporate the patient perspective; diverse designs for comparative effectiveness research and pharmacoepidemiology; systematic reviews and meta-analyses; pragmatic trials; cohort and case-only designs; as well as advanced statistical modeling and analysis (e.g., causal inference in observational studies, drug exposure modeling, health economic evaluations, and meta-epidemiological studies). This multidisciplinary team includes epidemiologists, biostatisticians, pharmacists, and clinicians.

Sabine Hoffmann is currently Head of the Statistical Consulting Unit (StaBLab) at Ludwig-Maximilians-Universität München, a position she has held since October 2024. Prior to that, she served as a Senior Academic Councillor at the Institute of Statistics  and as Interim Professor of Biostatistics. She earned her PhD in Biostatistics in 2017 from the Institut de Radioprotection et de Sûreté Nucléaire (France), focusing on Bayesian hierarchical methods to account for measurement error in low-dose ionizing radiation exposure studies applied to a cohort of uranium miners. Her research focuses on developing statistical methodologies to quantify the impact of measurement error and other sources of uncertainty, especially in routine and observational data, as well as collaborating with medical scientists to improve research reproducibility, open science practices, and methodological rigor.

Florian Naudet is a psychiatrist, meta-researcher and former post-doctoral fellow at METRICS (the Meta-research Innovation Center at Stanford). He's currently Professor of Therapeutics at Rennes University, and a senior member at the Institut Universitaire de France. His research interests are evaluating and developing methodological solutions to assess treatments in patients, primarily but not exclusive in psychiatric research. He has a strong interest in studying research wastes and data-sharing practices.