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Trevor Hastie - John A. Overdeck Professor and Professor of Statistics and of Health Research & Policy

Bio-X Affiliated Faculty

Dr. Hastie is known for his research in applied statistics, particularly in the fields of data mining, bioinformatics and machine learning. He has published four books and over 180 research articles in these areas. Prior to joining Stanford University in 1994, Hastie worked at AT&T Bell Laboratories for 9 years, where he helped develop the statistical modeling environment popular in the R computing system. He received his B.S. in statistics from Rhodes University in 1976, his M.S. from the University of Cape Town in 1979, and his Ph.D from Stanford in 1984. Professor Hastie is an elected fellow of the Institute of Mathematical Statistics, the American Statistical Association, the International Statistics Institute, the South African Statistical Association and the Royal Statistical Society. He has received a number of awards and honors, including the Myrto Lefkopolous award from Harvard in 1994.

Dr. Hastie specializes in applied nonparametric regression and classification, and he has written three books in this area: "Generalized Additive Models" (with R. Tibshirani, Chapman and Hall, 1991), and "Elements of Statistical Learning (second edition)" (with R. Tibshirani and J. Friedman, Springer 2009), and "An Introduction to Statistical Learning" (with G. James, D. Witten and R. Tibshirani, Springer 2013). He has also made contributions in statistical computing, co-editing (with J. Chambers) a large software library on modeling tools in the S language used in R and Splus ("Statistical Models in S", Wadsworth, 1992). His current research focuses on applied problems in biology and genomics, medicine and industry, in particular data mining, prediction and classification problems.