Overview:
Join us for our upcoming webinar exploring emerging opportunities for statisticians in AI education! Hear perspectives from statisticians who are actively involved in AI education initiatives, highlighting concrete examples, challenges, and best practices.
Speakers
Tian Zheng, Professor of Statistics at Columbia University
Nick Horton, Beitzel Professor of Technology and Society (Statistics and Data Science) at Amherst College
Moderator
Dr. Linglong Kong, Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta
Co-Organizers
Dr. Bei Jiang, University of Alberta and Dr. Hongtu Zhu, UNC at Chapel Hill
Abstract
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Curriculum Evolution: Rethinking how we teach statistics to both majors and non-statisticians to meet the demands of an AI-driven world.
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Interdisciplinary Research: Moving beyond theoretical silos to solve complex, real-world problems.
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Faculty Development: Preparing the next generation of researchers to lead in a landscape where the lines between data science, AI, and statistics are increasingly blurred.
Statisticians bring unique value to the AI table, from uncertainty quantification to causal inference. This session will identify current bottlenecks and offer a roadmap for statisticians to effectively contribute to and lead the AI educational revolution.
About the Speakers
Dr. Tian Zheng is Professor of Statistics at Columbia University. In her research, she develops novel methods for exploring and understanding patterns in complex data from different application domains such as biology, psychology, climate modeling, etc. Her 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, a Fellow of the Institute of Mathematical Statistics in 2022, and a Fellow of the American Association for the Advancement of Science in 2024. From 2017 to 2020, she served as Associate Director for Education at the Columbia Data Science Institute. From 2019 to 2025, she was chair of the Department of Statistics at Columbia. Professor Zheng is the recipient of the 2017 Columbia Presidential Award for Outstanding Teaching. In 2021, she was recognized with a Lenfest Distinguished Columbia Faculty Award, which honors the excellence of faculty as teachers and mentors of both undergraduate and graduate students. See Profile
Dr. Nick Horton is Beitzel Professor of Technology and Society (Statistics and Data Science) at Amherst College. He served as the editor of the Journal of Statistics and Data Science Education, was co-PI of the NSF-funded Data Science Corps Wrangle/Analyze/Visualize project, chaired the Committee of Presidents of Statistical Societies, co-chaired of the National Academies Committee on Applied and Theoretical Statistics, and chaired the National Academies Consensus Study on Data and Computing Competencies for K-12. Nick has published more than 200 papers and books and is a Fellow of the American Statistical Association, the Institute for Mathematical Statistics, and the American Association for the Advancement of Science. See Profile
About the Moderator
Dr. Linglong Kong is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, holding a Canada Research Chair in Statistical Learning and a Canada CIFAR AI Chair. He is a Fellow of the American Statistical Association (ASA) and the Alberta Machine Intelligence Institute (Amii), with over 120 peer-reviewed publications in leading journals and conferences such as AOS, JASA, JRSSB, NeurIPS, ICML, and ICLR. Dr. Kong received the 2025 CRM-SSC Prize for outstanding research in Canada. He serves as Associate Editor for several top journals, including JASA and AOAS, and has held leadership roles within the ASA and the Statistical Society of Canada. Dr. Kong’s research interests include high-dimensional and neuroimaging data analysis, statistical machine learning, robust statistics, quantile regression, trustworthy machine learning, and artificial intelligence for smart health. See Profile
About the NISS-CANSSI Collaborative Data Science Web Series:
The NISS-CANSSI Collaborative Data Science initiative that the National Institute of Statistical Sciences (NISS) in collaboration with the Canadian Statistical Sciences Institute (CANSSI) brings together experts from various fields to tackle complex data challenges through interdisciplinary teamwork and innovative methodologies.
Goals of the Initiative
The goal is to foster progress in:
- Developing new ideas for experimental and observational data-driven learning and discovery that address key questions at the cutting edge of science and scientific deduction;
- Quantifying and summarizing uncertainty in data-driven theories, as well as complex Data Science models, algorithms, and workflows; and
- Establishing new practices for scientific reproducibility and replicability through Data Science.
Learn more: NISS-CANSSI Collaborative Data Science
Event Type
- NISS Hosted