When Statistics Meets AI: Bayesian modeling of spatial biomedical data

：李琦玮（美国得克萨斯大学）
：2023-07-03 17:00
：海韵园实验楼105报告厅

报告人：李琦玮（美国得克萨斯大学）

时  间：2023年7月3日下午17:00

地  点：海韵园实验楼105报告厅

内容摘要:

Statistics relies more on human analyses with computer aids, while AI relies more on computer algorithms with aids from humans. Nevertheless, expanding the statistics concourse at each milestone provides new avenues for AI and creates new insides in statistics. This part incubates the findings initiated from either side of statistics or AI and benefits the other. In this talk, I will demonstrate how the marriage between spatial statistics and AI leads to more explainable and predictable paths from raw spatial biomedical data to conclusions.

The first part concerns the spatial modeling of AI-reconstructed pathology images. Recent developments in deep-learning methods have enabled us to identify and classify individual cells from digital pathology images at a large scale. The randomly distributed cells can be considered from a marked point process. I will present two novel Bayesian models for characterizing spatial correlations in a multi-type spatial point pattern. The new method provides a unique perspective for understanding the role of cell-cell interactions in cancer progression, demonstrated through a case study of 188 lung cancer patients.

The second part concerns the spatial modeling of the emerging spatially resolved transcriptomics data. Recent technology breakthroughs in spatial molecular profiling have enabled the comprehensive molecular characterization of single cells while preserving their spatial and morphological contexts. This new bioinformatics scenario advances our understanding of molecular and cellular spatial organizations in tissues, fueling the next generation of scientific discovery. I will focus on how to integrate information from AI tools into Bayesian models to address some key questions in this field, such as spatially variable gene detection and spatial domain identification.

个人简介：

Dr. Qiwei Li is an Assistant Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UTD). Before joining UTD in 2019, he was an Assistant Professor at the University of Texas Southwestern Medical Center. Dr. Li received his Ph.D. from the Department of Statistics at Rice University in 2016 under the supervision of Dr. Marina Vannucci. Dr. Li has been actively involved in developing Bayesian methodology to address critical biomedicine issues. Dr. Li has been very productive in high-dimensional count data modeling, spatial analysis, and shape analysis. The developed methods have been used to analyze spatial transcriptomcis data, microbiome data, and artificial intelligence-reconstructed medical imaging data. He has over 50 publications in top impact peer-reviewed journals such as the Annals of Applied Statistics, Biometrics, Biostatistics, Bioinformatics, and more in the past five years since he started his academic career. His research is also recognized by government agencies, including NIH and NSF, where he is serving as PI and co-PI for many grants.

 

联系人：胡杰