From AI-Ready Data to Precision Health Through Advances in Data Science

主讲人：Hongzhe Li

Dr. Hongzhe Li is Perelman Professor of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine at the University of Pennsylvania.  He is Vice Chair for Research Integration, Director of Center of Statistics in Big Data and former Chair of the Graduate Program in Biostatistic at Penn. He is also a Professor of Statistics and Data Science at the Wharton School. Dr. Li has been elected as a Fellow of the American Statistical Association (ASA), a Fellow of the Institute of Mathematical Statistics (IMS) and a Fellow of American Association for the Advancement of Science (AAAS). Dr. Li served on the Board of Scientific Counselors of the National Cancer Institute of NIH and regularly serves on various NIH study sections. He served as Chair of the Section on Statistics in Genomics and Genetics of the ASA and Co-Editor-in-Chief of Statistics in Biosciences.   Dr. Li’s research focuses on developing statistical and computational methods for analysis of large-scale genetic, genomics and metagenomics data and theory on high dimensional statistics.

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报告信息

时间 

2026年1月8日（周四） 

10:00

地点 

中国人民大学中关村校区

明德主楼1016

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报告摘要

The growing availability of large-scale, multimodal biomedical data has transformed precision health, creating new opportunities for prediction, discovery, and intervention. In this seminar, I discuss how statistical and data science methods enable precision health in the era of AI, with a focus on building and analyzing AI-ready health data. I begin by describing challenges in data engineering, integration, and governance for high-dimensional data arising from genomics, proteomics, single-cell technologies, imaging, electronic health records, and large population cohorts. I then present several examples from my work, including proteomics-based cardiovascular risk prediction, transfer learning and multicalibration across cohorts, integrative genomics and epigenomics analyses of kidney disease, and AI-driven modeling of cancer and aging data. Throughout, I emphasize the role of statistical thinking—study design, uncertainty quantification, generalizability, and validation—in ensuring that modern AI methods lead to reliable and interpretable scientific and clinical insights.