David Banks obtained a Ph.D. in Statistics in 1984. He won an NSF Postdoctoral Research Fellowship in the Mathematical Sciences, which he took at Berkeley, working with David Blackwell. In 1986 he was a visiting assistant lecturer at the University of Cambridge, and then joined the Department of Statistics at Carnegie Mellon in 1987. In 1997 he went to the National Institute of Standards and Technology, then served as Chief Statistician of the U.S. Department of Transportation, and finally joined the U.S. Food and Drug Administration in 2002. In 2003, he returned to academics at Duke University. He was the coordinating editor of the Journal of the American Statistical Association. He co-founded the journal Statistics and Public Policy and served as its editor. He co-founded the American Statistical Association's Sections on National Defense and Homeland Security and Text Analysis, and has chaired those sections, as well as the sections on Risk Analysis and Statistical Learning and Data Mining. David Banks is past-president of the Classification Society and the International Society for Business and Industrial Statistics. He has twice served on the Board of Directors of the American Statistical Association. He is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. He won the American Statistical Association's Founders Award, the De Groot Award, and gave the William Sealy Gosset and Deming Lectures. From January 2018 to Sept., 2021, he was the director of Statistical and Applied Mathematical Sciences Institute. His research areas include computational advertising, dynamic text networks, adversarial risk analysis (i.e., Bayesian behavioral game theory), human rights statistics, agent-based models, forensics, and certain topics in high-dimensional data analysis.

Dean P. Foster studied AI at Rutgers University and statistics at the University of Maryland before those fields converged into what is now commonly known as machine learning. He previously served on the faculty at the University of Chicago and the University of Pennsylvania before joining Amazon in New York City in 2015 as a Senior Principal Research Scientist. His research spans machine learning, reinforcement learning, large language models, statistics, and game theory. In game theory, he helped pioneer stochastic evolutionary game dynamics and calibrated learning, developing theoretical tools for proving convergence to equilibrium. His work on calibrated learning and no-internal-regret algorithms was among the earliest to establish convergence to correlated equilibrium and became foundational in theoretical machine learning. In statistics, he is widely known for contributions to large-scale regression problems, including early work on risk inflation in high-dimensional settings and the development of alpha-investing, which provides both a theoretical framework for variable selection and a fast algorithm suitable for streaming data. At Amazon, he founded a reinforcement learning team responsible for large-scale inventory and logistics optimization problems involving millions of products and hundreds of warehouses through multi-agent reinforcement learning systems. More recently, his work has focused on the use of large language models not only for code generation but also for theorem proving and software reasoning.

David Rosenberg leads the Machine Learning Strategy team in the Office of the CTO at Bloomberg. He was a co-author of the BloombergGPT research paper, which explored building a large language model tailored to the financial domain. He was previously an adjunct associate professor at NYU’s Center for Data Science, where he twice received the “Professor of the Year” award. Before joining Bloomberg, David served as Chief Scientist at Sense Networks, a location data analytics and mobile advertising company, and he served as scientific adviser to Discovereads, a book recommendation company first acquired by Goodreads and later Amazon. He holds a Ph.D. in statistics from UC Berkeley, an S.M. in applied mathematics from Harvard University, and a B.S. in mathematics from Yale University. He is currently based in Toronto.

Bin Yu is Chancellor’s Distinguished Professor in the Departments of Statistics and Electrical Engineering and Computer Sciences at University of California, Berkeley. A member of both the National Academy of Sciences and the American Academy of Arts and Sciences, she is widely recognized for pioneering the Predictability–Computability–Stability (PCS) framework for veridical data science, which has become an influential approach for transparent, reproducible, and trustworthy data science and AI. Her group develops efficient and interpretable ML/AI methods and theory, including influential contributions such as iterative random forests (iRF), contextual decomposition for transformers, adaptive wavelet distillation, and LoRA+ methods for deep learning fine-tuning. Her research spans statistical theory, interpretable machine learning, causal inference, and scientific discovery, with applications in neuroscience, genomics, remote sensing, and precision medicine through close interdisciplinary collaborations with domain experts.

Professor Yu received her B.S. in Mathematics from Peking University and her M.S. and Ph.D. in Statistics from University of California, Berkeley. Her major honors include the COPSS Distinguished Achievement Award and Lectureship, the COPSS E. L. Scott Award, and a Guggenheim Fellowship. She has delivered many distinguished lectures, including the Breiman Lecture at NeurIPS, the Wald Memorial Lectures of the Institute of Mathematical Statistics, and the COPSS Distinguished Achievement Award and Lectureship at JSM. Professor Yu has also served in numerous leadership roles in the statistics and data science communities, including President of the IMS, Chair of the Berkeley Statistics Department, and advisory and editorial positions for leading scientific institutes and journals.

Dr. Tian Zheng is Professor of Statistics at Columbia University. She obtained her Ph.D. from Columbia in 2002. In her research, she develops novel methods for exploring and understanding patterns in complex data from different application domains such as biology, psychology, climatology, etc. Her current projects are in the fields of statistical machine learning, spatiotemporal modeling, and social network analysis, collaborating with ecologists and earth scientists. Professor Zheng’s research has been recognized by the 2008 Outstanding Statistical Application Award from the American Statistical Association (ASA), the Mitchell Prize from ISBA, and a Google research award. She became a Fellow of the American Statistical Association in 2014. Professor Zheng is passionate about education and mentoring. From 2015-2016, she was one of the series creators for Columbia’s edX Massive Online Open Course (MOOC) series on data science. From 2017-2020, she was associate director for education of Columbia Data Science Institute. She led a number of education programs, including the MS in Data Science program at Columbia, data science capstone projects with data ethics components, DSI Scholars program that connects students with academic research projects in data science, the Collaboratory program for interdisciplinary data science curriculum development, a number of popular Data Science boot camps. She created DSI’s working group on Data Science Education and has been coordinating data science education efforts across Columbia. Professor Zheng is the recipient of the 2017 Columbia’s Presidential Award for Outstanding Teaching. In 2021, she was recognized by a Lenfest Distinguished Columbia Faculty Award that recognizes the excellence of faculty as teachers and mentors of both undergraduate and graduate students.