Robin Genuer is an associate professor (maître de conférences) in statistics at the School of Public Health (ISPED) of Bordeaux University. He defended a PhD about random forests and then worked on different aspects related to random forests (variable selection, behavior in the big data context, extension to longitudinal data and for the dynamic prediction problem) usually in an high-dimensional context with applications in genomic data analyses. He wrote, together with Jean-Michel Poggi, the book untitled « Random Forests with R » and its associated French version, and is the maintainer of the VSURF R package.

Pierre Geurts is Professor in computer science at the Montefiore Institute in the faculty of applied sciences of the University of Liège in Belgium. The general focus of his research is the design, the empirical, and theoretical analyses of supervised machine learning algorithms, mainly tree-based and deep learning methods. Over the years, his group has developed a broad expertise on the application of machine learning algorithms in various domains including systems biology, biomedical image analysis, computer networks, and digital humanities. For more than 20 years, he has developed a strong expertise on tree-based ensemble methods, random forests and boosting methods. His contributions around these methods include the extremely randomised tree algorithm, several adaptations of these methods to handle complex input and output spaces, and more recently a theoretical characterisation of some variable importance measures and their application to gene network inference.

Nathalie Vialaneix is senior researcher (Directrice de Recherche) in statistics at MIAT laboratory of the French National Institute for Agriculture, Food and Environment (INRAE). Her research interests are on the development of statistical and machine learning methods for molecular biology, with a specific focus on omics data integration and network inference and analyses. She has a general experience on random forests and their extensions in her field and contributed to understand its usage and limits for big data analysis. She is a member of SFdS and of SFBI.

Ruoqing Zhu is an Associate Professor at the department of statistics at University of Illinois Urbana Champaign (UIUC). He obtained his PhD in Biostatistics from University of North Carolina, Chapel Hill in 2013 and then spent two years at Yale University as a postdoctoral research associate before joining UIUC. His research focuses on personalized medicine, statistical machine learning, reinforcement learning, computationally intensive algorithms, and their applications in infectious diseases, nutrition, and cancer. He is an affiliated member of the Carle Illinois College of Medicine, the National Center for Supercomputing Applications and the Carl R. Woese Institute for Genomic Biology. Starting in 2022, he also serves as an Advisory Board Member at Prenosis, a company that focuses on managing acute diseases using machine learning and personalized medicine approaches.

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