题 目：Statistical Analysis of Shape Data in Large-Scale Biomedical Studies
报告人：朱宏图 教授
主持人：刘玉坤 教授
时 间：2022年10月14日（周五）09:00-10:00
地 点：Zoom会议ID：87374339181，密码：458830
报告人简介：
朱宏图博士是北卡罗来纳大学教堂山分校生物统计学、统计学、计算机和生物遗传学终身教授，曾任MD安德森癌症中心的诊断影像学Bao-Shan Jing讲席教授和生物统计学终身教授，滴滴出行首席统计学家。2000年获得香港中文大学统计学博士学位。主要研究领域为统计学习、医疗图像处理、精准医疗、生物统计、人工智能和大数据分析。2011年当选美国统计学会和数理统计学会会士。2016年荣获德克萨斯州癌症预防与研究中心杰出研究奖。2019年因强化学习在网约车出行中的应用荣获Daniel Wagner杰出应用奖。在多个大型医疗研究项目中担任统计分析师，并提供实验设计、数据分析和新方法开发。现有高水平期刊论文300多篇，包括Nature，Science， Cell， Nature Genetics，Nature Communication， Nature Neuroscience，JAMA Psychiatry，PNAS，JMLR， AOS以及JRSSB；高水平会议论文45篇，包括KDD，NIPS，ICDM，AAAI，MICCAI以及IPMI。担任多个国际顶级会议的区域主席，包括Information Processing in Medical Imaging。担任（过）多个国际顶级期刊的编委，包括Statistica Sinica，JRSSB，Biometrics，Annals of Statistics和Journal of American Statistical Association。
报告内容简介：
In medical imaging analysis and computer vision, there is a growing interest in analyzing various manifold-valued data including 3D rotations, planar shapes, oriented or directed directions, the Grassmann manifold, deformation field, symmetric positive definite (SPD) matrices and medial shape representations (m-rep) of subcortical structures. Particularly, the scientific interests of most population studies focus on establishing the associations between a set of covariates (e.g., diagnostic status, age, genetic variates, and gender) and manifold-valued data for characterizing brain structure and shape differences, thus requiring a statistical modeling framework for manifold-valued data. The aim of this talk is to introduce a series of statistical models for the analysis of manifold-valued data as responses in a Riemannian manifold and their associations with a set of covariates, such as age, genetic variates, and gender, in Euclidean space. Because manifold-valued data do not form a vector space, directly applying classical multivariate regression may be inadequate in establishing the relationship between manifold-valued data and covariates of interest, such as age and gender, in real applications. We apply our methods to the detection of the difference in the morphological changes of cortical and subcortical shape, the evolution of shape changes, and the genetic architecture of brain shape.