Overview
Statistics and AI form a two-way street: AI can accelerate statistical discovery, and statistical guarantees make AI reliable in practice. This discussion asks where and how Statistics should lead and explore priorities, methodologies, and the most impactful opportunities for leadership.
Speakers
Anne Qu, Professor of Statistics and Applied Probability, University of California at Santa Barbara
Tianxi Cai, John Rock Professor of Population and Translational Data Sciences in the Department of Biostatistics at Harvard T.H. Chan School of Public Health
Moderator
Bei Jiang, Assistant Professor, Mathematics & Statistical Sciences, University of Alberta
About the Speakers
Annie Qu, PhD is a Professor in the Department of Statistics and Applied Probability, University of California, Santa Barbara starting July 2025. She received her Ph.D. in Statistics from the Pennsylvania State University in 1998. Qu’s research focuses on solving fundamental issues regarding unstructured large-scale data, developing cutting-edge statistical methods and theory in machine learning and algorithms on text sentiment analysis, automatic tagging and summarization, recommender systems, tensor imaging data and network data analyses for complex heterogeneous data, and achieving the extraction of essential information from large volume high-dimensional data. Her research has impacts in many different fields such as biomedical studies, genomic research, public health research, and social and political sciences. Before she joins the UC Santa Barbara, Dr. Qu was Chancellor's Professor at UC Irvine, and Data Science Founder Professor of Statistics and the Director of the Illinois Statistics Office at the University of Illinois at Urbana-Champaign. She was awarded as Brad and Karen Smith Professorial Scholar by the College of LAS at UIUC, a recipient of the NSF Career award in 2004-2009, and is a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, and a fellow of American Association for the Advancement of Science. She is current Co-Editor of Journal of the American Statistical Association, Theory and Methods. See profile
Tianxi Cai, PhD is the John Rock Professor of Population and Translational Data Sciences in the Department of Biostatistics at Harvard T.H. Chan School of Public Health. Dr. Cai’s current research interests are mainly in the area of biomarker evaluation; model selection and validation; prediction methods; personalized medicine in disease diagnosis, prognosis and treatment; statistical inference with high dimensional data; and survival analysis. In addition to her methdological research, Dr. Cai also collaborates with the I2B2 (Informatics for Integrating Biology and the Bedside) center on developing a scalable informatics framework that will bridge clinical research data and the vast data banks arising from basic science research in order to better understand the genetic bases of complex diseases. See profile
About the Moderator
Bei Jiang, PhD, uses statistical analysis and statistical machine learning to decode complex health data, searching for better patient outcomes. Her work has involved analyzing brain imaging, which could help build effective treatment plans for people with psychiatric disorders. Her research also focuses on integrating Bayesian modelling with statistical machine learning methods, aiming to overcome some of the roadblocks of classical statistical inference. Jiang earned her MSc in Biostatistics at the University of Alberta in 2008 before completing her PhD at the University of Michigan. She returned to Edmonton in 2015 — first as an assistant professor and now an associate professor — at the U of A's Mathematical and Statistical Sciences faculty. In 2015, she was also named a research fellow with the U.S.-based Statistical and Applied Mathematical Sciences Institute. Her areas of research includes: Artificial intelligence, machine learning, statistical machine learning, bayesian hierarchical modelling, functional and imaging data analysis, kernel machine regression, modelling of health outcome data, and biostatistics. See Profile
About the COPSS-NISS Leadership Webinar Series
COPSS (Committee of the Presidents of Statistical Societies) and NISS have come together to organize and host a new webinar series focusing on leadership in statistics and data science. Plan to attend these webinars every month during the academic year! Visit the COPSS-NISS Leadership Series Page for previous webinars.
The COPSS-NISS Leadership Webinar Series is co-organized by the Committee of the Presidents of Statistical Societies (COPSS) Emerging Leaders in Statistics and the National Institute of Statistical Sciences (NISS). The purpose of the webinar series is to promote leadership skills for members of the statistical societies at any stage in their careers. The series features conversations with leaders throughout the discipline, including leaders from major academic and government institutions, and companies. Invited speakers share their leadership stories and answer questions about their experiences. Each webinar is moderated by a member of the COPSS Emerging Leaders in Statistics program.
Access the Full COPSS-NISS Leadership Webinar Series YouTube Playlist | COPSS-NISS Leadership Webinar Series: https://www.youtube.com/playlist?list=PLoRtupvDJTjvFukMcO6NfDr0GvxDsIj81
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- NISS Hosted
- NISS Sponsored
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