Charles Bouveyron is Full Professor of Statistics with Université Côte d’Azur and the director of the Institut 3IA Côte d’Azur, one of the nine French institutes in Artificial Intelligence. He is the head of the Maasai research team, a joint team between INRIA and Université Côte d’Azur, gathering mathematicians and computer scientists for proposing innovative models and algorithms for Artificial Intelligence. Since 2019, he holds a chair in Artificial Intelligence at Institut 3IA Côte d’Azur on unsupervised learning with heterogenous data. His research interests include high-dimensional statistical learning, adaptive learning, statistical network analysis, learning from functional or complex data, deep latent variable models, with applications in medicine, image analysis and digital humanities. He has published extensively on these topics (more than 50 journal articles) and he is author of the monograph “Model-based Clustering and Classification for Data Science” (Cambridge University Press, 2019). He is the founding organizer of the series of workshops StatLearn. Previously, he worked at Université Paris Descartes (Full Professor, 2013-2017), Université Paris 1 Panthéon-Sorbonne (Ass. Professor, 2007-2013) and Acadia University (Postdoctoral researcher, 2006-2007). He received the Ph.D. degree in 2006 from Université Grenoble 1 (France) for his work on high-dimensional classification.

Kaize Ding is an Assistant Professor in the Department of Statistics and Data Science at Northwestern University. His research interests are generally in data mining, machine learning, and large foundation models. His recent research focus is to build reliable and efficient AI systems for autonomous decision-making, with the applications in domains such as healthcare/biomedicine, urban/environmental computings, etc. Kaize’s research has been published at top-tier conferences and journals (e.g., AAAI, EMNLP, IJCAI, KDD, NeurIPS, TheWebConf, and TNNLS), and has been recognized with several prestigious awards and honors, including Amazon Research Awards, AAAI New Faculty Highlights, SDM Best Posters Award, etc.

Dr, Inge Svein Helland is professor emeritus at the University of Oslo. He got his master’s degree in statistics from the University of Bergen in1973 and his Dr. Philos. degree from the University of Oso in 1980. He has been professor in statistics at the Agricultural University of Norway and at the University of Oslo. His research covers more than 100 publications, most of them in various areas in applied and theoretical statistics. During the last 10 years, he has worked with the foundation of quantum theory, which has resulted in 4 books and various articles in leading journals in theoretical physics.

Jana Jurečková is Professor Emerita of Charles University in Prague, Czech Republic, where she worked in Department of Probability and Mathematical Statistics. Besides that she works as a Senior Research Fellow in the Institute of Information Theory and Automation, the Czech Academy of Sciences, since 2018. She received PhD in 1977 and the degreee DrSc in 1984 in Charles University in Prague; and is a elected member of the Learned Society of the Czech Republic, of ISI, and Fellow of IMS. As a Visiting Professor she worked in Bordeaux and Toulouse (France), in Neuchatel (Switzerland), in Chapel Hill (NC, USA). Besides that she had an intensive cooperation in Brussels (Belgium), in Ottawa (Canada), in Urbana-Champaign (Illinois, USA), in Freiburg (Germany), and elsewhere.
Her Fields of Interest: Analytical Statistics, Probability, Estimation and Hypotheses Testing, Robust and Nonparametric Statistical Methods, Extreme Value Theory. In this area she was an Advisor of 13 PhD students, worked in editorial boards of statistical journals and contributed to organizations of conferences. She is a coauthor of 4 monographs and of more than 160 journal publications, rather frequently cited.

Hong Pan, a dedicated professional in data science and statistics, was born to a family of educators. He attended Shanghai Jiao Tong University, where he studied Biomedical Engineering, and then joined Purdue University in the U.S. for his PhD program in Electrical and Computer Engineering.
After obtaining his PhD, Hong first joined Cornell University Medical College as a faculty member, where he conducted and oversaw technical, analytic, and engineering aspects of human in vivo functional and molecular neuroimaging research and trained multidisciplinary students, research fellows, and clinician scientists; and then moved to Harvard Medical School as a faculty member where he further his invention to innovation technology transfer journey in data science applications for medical imaging.
For over 25 years, Hong has been a leader in data science efforts, serving as the subject matter expert on over 20 federal and institutional projects. His influence and impact in the field, particularly his expertise in AI/ML algorithms and advanced statistics, have been instrumental in developing statistical, data-driven diagnostic tools for guiding the treatment of brain disorders. He has created best practice approaches for optimized data acquisition, data science solutions for biomarker discovery, and automated analytics and informatics pipelines based on functional neuroimaging methodology. His work has resulted in 4 patents, a successful spin-out startup, and earned him the Mass General Brigham Excellence in Innovation Award twice and Brigham and Women’s Hospital’s Pillar Award in Research & Innovation, with over 60 journal publications, solidifying his professional standing in the field.
In 2023, Hong joined Simmons University, a women-focused liberal arts college in Boston, as a faculty member and started focusing on full-time teaching in statistics and data science.