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When old statistical ideas meet modern data science

2018/11/15 10:00  -  2018/11/15 11:00

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When old statistical ideas meet modern data science

Speaker: Weining Shen, University of California, Irvine

Host: Fengnan Gao, School of Data Science, Fudan University

Time: 10:00-11:00, November 15, 2018

Location: Zibin N102, Fudan University

Abstract: 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.

Bio: Weining Shen is 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.


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