嘉宾简介 Introduction
Dr. Zhu is a tenured Professor of Biostatistics at the University of North Carolina at Chapel Hill and was DiDiFellow and Chief Scientist of Statistics at DiDi Chuxing, and Endowed Bao-Shan Jing Professorship in Diagnostic Imaging and a tenured professor of biostatistics at MD Anderson Cancer Center. Dr. Zhu got his Ph.D. degree in statistics from the Chinese University of Hong Kong in 2000. He is an internationally recognized expert in statistical learning, medical image analysis, genetic/genomic analysis, precision medicine, biostatistics, artificial intelligence, and big data analytics. He has been an elected Fellow of American Statistical Association and Institute of Mathematical Statistics since 2011. He received an established investigator award from Cancer Prevention & Research Institute of Texas in 2016 and received Daniel Wagner Prize for Excellent in Operations Research Practice with his colleagues at DiDi in 2019. He has published more than 285 papers in top journals including Nature, Science, Cell, Nature Genetics, Nature Communication, JASA, Biometrika, JMLR, AOS, and JRSSB, as well as 42 conference papers in top conferences including NIPS, AAAI, KDD, ICDM, MICCAI, and IPMI. He has served/is serving as a chair or area chair of top international conferences including Information Processing in Medical Imaging, as well as an editorial board member of premier international journals including Statistica Sinica, JRSSB, Annals of Statistics, and Journal of American Statistical Association.
报告简介 Course Content
Recently the UK Biobank study has conducted brain MRI imaging scans of over 40,000 participants. In addition, publicly available imaging genetic datasets also emerge from several other independent studies. We collected massive individual-level MRI data from different data resources, harmonized image processing procedures, and conducted the largest genetic studies so far for various neuroimaging traits from different structural and functional modalities. In this talk, we showcase novel clinical findings from our largescale analyses, such as the shared genetic influences among brain structures, functions, and a wide spectrum of clinical outcomes. We establish genetic mappings from hundreds of anatomical brain regions onto their corresponding functional connectivities, and further onto complex mental disorders, which may transverse our understanding of the disease emergence and development. We also discuss methodological challenges we have faced when processing these biobank-scale datasets and highlight opportunities to utilize the learned knowledges in downstream analyses for disease predictions and pathway analyses. This presentation is based on a series of works of the UNC BIG-S2 lab. Our results can be easily browsed through the Brain Imaging Genetics Knowledge Portal (BIG-KP) (https://bigkp.org/).
讲座海报 Poster
