Concha Bielza is a Full Professor with the Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid since 2010. She received the M.S. degree in Mathematics (statistics) from Universidad Complutense de Madrid, Spain, in 1989 and the Ph.D. degree in Computer Science from Universidad Politécnica de Madrid, in 1996 (extraordinary doctorate award).

She co-leads the Computational Intelligence Group (CIG) at UPM since its foundation in 2010 and co directed the ELLIS Unit Madrid (2022–2024). She has participated in 65 public research projects—including the EU’s 10-year Human Brain Project—and 41 industry contracts (Telefónica I+D, Abbott, Bank of Santander, Panda Security, Repsol, ArcelorMittal). She is co-inventor of a patent on lung adenocarcinoma. She has delivered 44 invited talks/seminars and 11 plenary talks in conferences, served on 91 program committees, and organized 19 scientific events—including Program Chair of CAEPIA-2013 and Journal Track Chair of ECML-PKDD 2015 and 2025. She has published 160+ journal papers, supervised 24 PhD and 76 Master theses. Her h-index is 36 (Web of Science) and 46 (Google Scholar) with 6,192 and 11,260 citations, respectively. During the last years she coauthors two books: “Data-Driven Computational Neuroscience” (2021, Cambridge University Press) and “Industrial Applications of Machine Learning” (2019, CRC Press), translated to Chinese in 2023. She is associate editor of Neuroinformatics and Frontiers in Computational Neuroscience. She co-leads the UPM- Machine Learning and Advanced Statistics Summerschool (18th edition in 2026) with more than 80 international students every year.

Her research interests are primarily in the areas of probabilistic graphical models, decision analysis, causality, interpretability, metaheuristics for optimization, machine learning, Bayesian networks, multi-label classification, clustering, spatial and directional statistics, and real applications, like biomedicine, bioinformatics, neuroscience, industry, agriculture, service quality and industry 4.0. She has been awarded the National Award of Statistics (2024), Fellow of the Asia-Pacific Artificial Intelligence Association (2024), ELLIS Fellow (2023), and UPM Research Award (2014). In 2021 she joined the Scientific Advisory Board of the Norwegian Research Center for AI Innovation (NorwAI) —as its only Spanish member—, chaired the NNF Grand AI Challenge 2025 (Novo Nordisk Foundation, Denmark), and in 2025 was appointed external expert for the selection of a new Max Planck School in Artificial Intelligence.

Dr. Laura Trinchera is a Professor of Statistics and Data Science at NEOMA Business School in France where she leads the research center (Area of Excellence) in AI, Data Science & Business.

She holds a Master’s degree in Business and Economics (2004) and a PhD in Statistics (2008) from the University of Naples Federico II, Italy. Her research focuses on Data Sciences and Statistical Learning methods, with a focus on Structural Equation Modeling, PLS Methods, classification algorithms and psychometric methods. Her research has been published in internationally recognized journals such as Structural Equation Modeling: A Multidisciplinary Journal, Journal of Production Economics, Journal of Organizational Behavior, Recherche et Applications en Marketing, International Journal of Information Management and Management Decision. Also contributed to the Handbook of Partial Least Squares: Concepts, Methods and Applications.

She has been a visiting researcher at several esteemed institutions, including the University of California, Santa Barbara, the University of Michigan, Ann Arbor, the University of Hamburg, Charles University in Prague, HEC School of Management in Paris, and has served as an external lecturer at ESSEC Business School, Sciences Po Paris, and Sorbonne University in Abu Dhabi.

Sébastien Lê is an Associate Professor of Statistics at L’Institut Agro Rennes-Angers and a member of IRMAR (UMR 6625, University of Rennes, France). Trained as a statistician (ISUP) with a PhD in Applied Mathematics from Université Paris-Dauphine, he has spent more than two decades advancing multivariate methods for sensory science and consumer research, from confidence ellipses and holistic approaches such as Napping and Sorting to dynamic protocols like digit-tracking and the Holos environment. He is a co-author of the widely used R packages FactoMineR and SensoMineR (now integrated into jamovi), has published 50+ scientific papers, and co-authored the reference books *Analyzing Sensory Data with R* and *Exploratory Multivariate Analysis by Example Using R*. He currently contributes to the PLAT4TERFOOD project, applying data-driven approaches to understand—and help shift—consumer behavior within local food systems and broader food transitions.

Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks. After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.

Dr. Pascal Schlich has been with INRAE (National Research Institute for Agriculture, Food and Environment) for 40 years and is now on a position of emeritus director of research. In the first half of his career, he contributed to the birth of the Sensometrics Society and developed innovative methods for the collection and the analysis of sensory data. Then, he served for more than 20 years as scientific director of the ChemoSens platform at CSGA (Center for Taste and Feeding Science) in Dijon, France. He introduced and promoted TDS (Temporal Dominance of Sensations), developed sensory databases, coordinated national consumer research projects on sensory education and preferences toward fat, salt and sweet sensations and developed software in SAS and R. Pascal also taught statistics for sensory analysis in several universities in France, directed 17 Ph.D and provided several major companies with consulting.