Plenary and Keynote Speakers
7th International Conference on Statistics: Theory and Applications (ICSTA 2025)
We are pleased to announce the plenary and keynote speakers for the 7th International Conference on Statistics: Theory and Applications:
Dr. Sheng Li
University of Virginia, USA
Plenary Speaker
Sheng Li is a Quantitative Foundation Associate Professor of Data Science and an Associate Professor of Computer Science (by courtesy) at the University of Virginia (UVA). He was an Assistant Professor of Data Science at UVA from 2022 to 2023, an Assistant Professor of Computer Science at the University of Georgia from 2018 to 2022, and a Data Scientist at Adobe Research from 2017 to 2018. He received his PhD degree in Computer Engineering from Northeastern University in 2017 and received his master’s degree and bachelor’s degree from School of Computer Science at Nanjing University of Posts and Telecommunications in 2012 and 2010, respectively. His recent research interests include Trustworthy AI, Causal Inference, Large Foundation Models, and Vision-Language Modeling. He has published over 180 papers, and has received over 10 research awards, such as the INNS Aharon Katzir Young Investigator Award, Fred C. Davidson Early Career Scholar Award, Adobe Data Science Research Award, Cisco Faculty Research Award, and SDM Best Paper Award. He currently serves as Associate Editor for six journals such as Transactions on Machine Learning Research (TMLR) and IEEE Trans. Neural Networks and Learning Systems (TNNLS), and serves as an Area Chair for IJCAI, NeurIPS, ICML, and ICLR.
Topic of Keynote: Causality for Trustworthy Artificial Intelligence
Plenary Abstract
Dr. Leonard Stefanski
North Carolina State University, USA
Plenary Speaker
Dr. Stefanski received a PhD in 1984 from the University of North Carolina, Chapel Hill, having joined the faculty at NC State University in 1986 where he has served as Graduate Program Director, Associate Department Head, and Department Head. He was Editor of the The Journal of the American Statistical Association (JASA T&M), and has served on state and national committees and boards including the BEIR VII Committee on the Health Risks from Exposure to Low Levels of Ionizing Radiation, National Academy of Sciences, and the North Carolina Forensic Science Advisory Board. His research is in the general area of statistical inference with emphasis on the analysis of data measured with error, robust statistical procedures, and variable and model selection. Dr. Stefanski is an elected Fellow of the American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS), and the American Association for the Advancement of Science (AAAS).
Topic of Keynote: Fractional Ridge Regression
View Abstract
Dr. Charles Bouveyron
Université Côte d'Azur, France
Keynote Speaker
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.
Topic of Keynote: Unsupervised Learning with Communication Networks: From Stochastic Block Models to Deep Latent Variables Models
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Dr. Kaize Ding
Northwestern University, USA
Keynote Speaker
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.
Topic of Keynote: Data-Efficient Graph Learning
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Dr. Inge S. Helland
University of Oslo, Norway
Keynote Speaker
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.
Topic of Keynote: On Quantum Foundation, As Seen By A Statistician
Keynote Abstract
Dr. Jana Jureckova
Charles University, Cezh Republic
Keynote Speaker
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.
Topic of Keynote: Quantile Functionals as Measures of Social, Health and Technical Events
Keynote Abstract
Dr. Hong Pan
Simmons University, USA
Keynote Speaker
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.
Topic of Keynote: The Unlikely Revolutionary: You, The Statistician — From ECT Confusion To AI Revolution: Discover How Statisticians Transformed Messy Medical Data into Life-Saving Insights View Abstract
Important information for accompanying person(s): Please be informed that the accompanying person can NOT be a co-author.
Co-authors, regardless if 1 author is attending, must pay the full registration fee.
The accompany person fee is only for spouses and/or children. Please contact us if you are unsure.
Virtual registration fee includes the following:
- Publication of 1 accepted paper in the proceedings. Publication of each additional paper requires a €150 EUR registration
- Access to all the sessions of the conference