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Trevor Hastie is the John A Overdeck Professor of Statistics atStanford University. Hastie is known for his research in appliedstatistics, particularly in the fields of statistical modeling, bioinformaticsand machine learning. He has published six books and over 200research articles in these areas. Prior to joining StanfordUniversity in 1994, Hastie worked at AT&T Bell Laboratories for nineyears, where he contributed to the development of the statistical modeling environmentpopular in the R computing system. He received a B.Sc. (hons) in statisticsfrom Rhodes University in 1976, a M.Sc. from the University of CapeTown in 1979, and a Ph.D from Stanford in 1984. In 2018 he was electedto the U.S. National Academy of Sciences. He is a dual citizen of theUnited States and South Africa.
Trevor Hastie specializes in applied statistical modeling, and he has written five books in this area:"Generalized Additive Models" (with R. Tibshirani, Chapman and Hall,1991), "Elements of Statistical Learning (second edition)" (with R. Tibshirani and J. Friedman, Springer 2009), "An Introduction to Statistical Learning" (with G. James, D. Witten and R. Tibshirani, Springer 2013), "Statistical Learning with Sparsity" (with R. Tibshirani and M. Wainwright, CRC Press 2015) and "Computer Age Statistical Inference" (with B. Efron, Cambridge, 2016). He has also made contributions in statistical computing, co-editing (with J. Chambers) a large softwarelibrary on modeling tools in the S language used in the R computing environment ("Statistical Models in S", Wadsworth, 1992). His current researchfocuses on applied problems in biology and genomics, medicine andindustry, in particular data modeling, prediction and classificationproblems.