时 间：2022年11月7日 14:00-15:30
地 点：腾讯会议ID：701-626-379
报告人：唐年胜 教授
主持人：唐炎林 研究员
摘 要：
With the recently developed medical imaging technology, brain images are captured through various scanners. Magnetic resonance image (MRI) and function magnetic resonance image (fMRI) are two widely-used imaging data sources for studying brain disease. In disease diagnosis study, disease prediction based on MRI and fMRI data has received considerable attention over the past years. A key challenging in analyzing MRI and fMRI data is to alleviate the well-known curse of dimensionality. Many Bayesian methods have been developed to address the issue. This paper aims to introduce variational Bayesian approaches to explore the relationship between regions of interest (ROIs) and some specified disease based on high-dimensional generalized linear models, ultrahigh-dimensional generalized tensor regression models, and high-dimensional gaussian graphical models. Some examples associated with MRI and fMRI data analysis are illustrated.
报告人简介：
唐年胜，教授、博导，国家杰出青年科学基金获得者、教育部“长江学者”奖励计划特聘教授、国家百千万人才工程暨有突出贡献中青年专家享受国务院政府特殊津贴，国际统计学会推选会员、国际数理统计学会会士（IMS Fellow），国家自然科学基金委数学天元基金学术领导小组成员、教育部高等学校统计学类专业教学指导委员会委员。先后主持国家杰出青年科学基金、国家自然科学基金重点项目等基金；发表学术论文170余篇（其中SCI收录134篇），在科学出版社等出版学术专著4部、译著2部、教材1部，主编出版英文书籍2部；获省部级科技奖励10项。主要研究方向包括：大数据统计学习、贝叶斯统计分析、缺失数据分析、高维数据分析、生物统计等。