报 告 人：沈韡凝 博士

报告时间：2019年9月26日    15:00 - 16:00

报告地点：校本部东区计算机大楼1001室

邀 请 人：谢 江 博士

报告简介：

Modern scientific applications have generated many data sets of complex nature, such as high dimensionality, heterogeneity and unknown structure of interest. In this talk, I will discuss a few ideas on extending classical statistical methods such as regression, the principal component analysis, expectation maximization, and mixture model, to accommodate challenges in those applications. Theoretical properties, numerical results, and applications in biomedical studies will be discussed.

报告人简介：

  Weining Shen is an assistant professor of Statistics at University of California, Irvine. He received his PhD from North Carolina State University in 2013, and his thesis won the Leonard J. Savage Dissertation Award. In 2013-2015, he was a postdoctoral fellow in Department of Biostatistics, M.D. Anderson Cancer Center. Prof. Shen’s research interest includes Bayesian methods, high-dimensional models, and applications in neuroscience, biology and disease studies.