American Statistical Association
Sufficient dimension reduction has a proven track record of aiding the analysis of high-dimensional complex data. It transforms the problem to a low dimensional projection, while retaining full regression information and imposing few probabilistic models. In this talk, we start with a brief overview of the area of sufficient dimension reduction. We then present some recent developments in both methodology and applications in genetics, biomedical studies, and neuroimaging analysis.
I obtained my B.E. in Electrical Engineering from Zhejiang University, P.R. China, in 1998, and my Ph.D. in Statistics from the School of Statistics, University of Minnesota, in 2003. Dr. R. Dennis Cook and Dr. Christopher J. Nachtsheim were my thesis advisors. I then worked as a Postdoctoral Researcher at Dr. P.J. Hagerman Lab, School of Medicine, University of California, Davis. I joined the Department of Statistics, North Carolina State University, in August 2005, as an Assistant Professor in Statistics.
|Date:||Thursday, April 7, 2011|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York