Professor Rebecca W. Doerge, Director - Department of Statistics - Purdue University Skip to main content

Professor Rebecca W. Doerge, Director

Rebecca Doerge

R.W. Doerge is a Professor of Statistics and Agronomy at Purdue University, and holds faculty appoints in both the College of Science and the College of Agriculture. Prior to joining Purdue, Dr. Doerge was a postdoctoral fellow at Cornell University (1993-1995). Dr. Doerge holds a B.S. and MS in Mathematics from the University of Utah, and received a Ph.D. in Statistics (1993) from North Carolina State University. Since joining Purdue University in 1995 Dr. Doerge has been awarded the Teaching for Tomorrow Award (1996); Outstanding Assistant Professor for Excellence in Teaching and Research Award (1997); Outstanding Teacher of Undergraduates in the College of Science Award (1998). In 2001 R.W. Doerge was named a University Scholar for both the College of Science and the College of Agriculture at Purdue (2001). Dr. Doerge is a member of AAAS, the American Statistics Association, the International Biometrics Society, and the Genetics Society of America. R.W. Doerge was the Chair of the Genomics Committee at Purdue University from 1997-2003, is a member of the organizing committee for The International Plant and Animal Genome Conference since 1998, and is the Chair of the 2005 Gordon Conference on Quantitative Genetics and Genomics. Dr. Doerge serves on the Editorial Boards of Statistical Applications in Genetics and Molecular Biology, Genetics, and The Plant Cell. Although Dr. Doerge's formal training is in Mathematics and Statistics, her research lies on the interdisciplinary boundaries of many fields. Currently Dr. Doerge's research program encompasses four broad areas that define much of Statistical Bioinformatics: development of statistical methodology for genetic mapping and quantitative trait loci (QTL) location; applying up to date genetic mapping and QTL methodology to real experimental data; assessing genetic variation and diversity of populations; and understanding and analyzing both gene expression and protein expression data for the purpose of investigating genomic/biologically based questions. For more information, please see her faculty page and homepage.

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