Freeman Spogli Institute for International Studies Center for Health Policy/Center for Primary Care and Outcomes Research Stanford University


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Richard A. Olshen, PhD   Download vCard
Professor of Health Research & Policy (Biostatistics) and, by courtesy, of Statistics and of Electrical Engineering and Stanford Health Policy Associate

Health Research and Policy
Redwood Bldg, T138C
Stanford, California 94305-5404

olshen@stanford.edu
(650) 725-2241 (voice)
(650) 725-6951 (fax)


Research Interests
genetic predisposition for hypertension and cardiovascular disease; confidence regions for functionals of ROC curves; the compression and classification of medical images; the study of kidney disease


Professor Olshen is a Fellow of the Institute of Mathematical Statistics, of the American Statistical Association, and the American Association for the Advancement of Science and is a Senior Member of the Institute of Electrical and Electronics Engineers. He has been a Guggenheim Fellow and the recipient of a Research Scholar in Cancer Award from the American Cancer Society. His interests include the development of statistical methods for prediction and the assessment of accuracy. He is one of the developers of CARTª binary tree-structured methods for classification, regression, and probability class estimation and of their extensions to survival analysis and clustering. In collaboration with others, he has studied these algorithms theoretically and has applied them to the computer-aided diagnosis of heart attack, as well as to making prognoses for patients with lymphoma, extracting features of organic compounds that tend to make them ulcerogenic, to data compression and the automated detection attempt to find the genes that predispose to hypertension, and to the definition of health states in health services research. His current research also involves the development of parsimonious models for describing longitudinal data, especially as they apply to understanding autoimmune disease of the kidney. Typically, these consist of the sum of an overall mean function and subject-specific coefficients of suitably smoothed eigenfunctions of residuals. In the past, he collaborated with Alan Garber in developing technologies for tracking cholesterol longitudinally in time and quantifying the accuracy of findings. Their ideas are now finding wide-ranging application.

Stanford Departments
Health Research and Policy; Electrical Engineering; Statistics

Other affiliations
Institute of Mathematical Statistics, American Statistical Association, American Association for the Advancement of Science, Institute of Electrical and Electronics Engineers