Statistics Seminar - 09/19/25

Abstract:

Statistics traditionally emphasizes human-driven analysis supported by computational tools, whereas AI primarily depends on computer algorithms with guidance from human insight. Nonetheless, each milestone in statistical development opens new frontiers for AI and offers fresh perspectives within statistics itself. This interplay fosters discoveries initiated from either domain that ultimately enrich the other. In this talk, I will illustrate how the integration of statistical spatial and shape analysis and AI enables more interpretable and predictive pathways from histopathology images to clinically meaningful insights. Recent advances in deep learning have made it possible to detect and classify tissue regions and individual cells at scale from digital histopathology images. I will introduce several novel AI-powered Bayesian models, including a Bayesian nonparametric model, for analyzing these new heterogeneous spatial data. These methods offer new insights into cell-cell interactions, spatial cellular architecture, and tumor boundaries in the context of cancer progression, supported by multiple case studies.

About the Speaker:

Dr. Qiwei Li is an Associate Professor in the Department of Mathematical Sciences at the University of Texas at Dallas. His academic journey includes earning his M.A. and Ph.D. in Statistics from Rice University in 2015 and 2016, respectively, followed by postdoc and faculty at the University of Texas Southwestern Medical Center. Throughout his career, Dr. Li has been engaged in pioneering novel Bayesian statistical methodologies tailored for the analysis of complex biomedical data. Over the past six years, his research efforts have resulted in the publication of over 50 peer-reviewed scholarly works among the top statistics and biostatistics journals. Moreover, his contributions to the field have garnered recognition from government agencies such as the NIH and NSF, where he has had the privilege of serving PI and co-I on many grants.