报告题目：Variational Bayesian Learning for Medical Imaging data
报 告 人：唐年胜 教授
报告时间：2022年7月5日 星期二 下午14:00-15:00
报告地点：腾讯会议 ID：799-408-566 会议密码：0705
报告摘要：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，云南大学教授，博士生导师，数学与统计学院院长。“国家杰出青年科学基金”获得者，教育部“长江学者”特聘教授，教育部“新世纪优秀人才”，国家百千万人才工程暨有突出贡献中青年科学家，享受国务院特殊津贴。国际统计学会推荐会员，国际数理统计学会会士，在Journal of the American Statistical Association、Annals of Statistics、Biometrika等学术期刊发表论文170余篇，出版专著4部。