2014 Parzen Prize
Texas A&M University, Department of Statistics proudly awards the 2014 Emanuel and Carol Parzen Prize for Statistical Innovation to
Dr. Trevor J. Hastie
John A. Overdeck Professor of Mathematical Sciences
Department of Statistics
Stanford University
Trevor Hastie received his university education from Rhodes University, South Africa (BS), University of Cape Town (MS), and Stanford University (Ph.D Statistics 1984).
His first employment was with the South African Medical Research
Council in 1977, during which time he earned his MS from UCT. In 1979 he spent a year interning at the London School of Hygiene and Tropical Medicine, the Johnson Space Center in Houston Texas, and the Biomath department at Oxford University. He joined the Ph.D program at Stanford University in 1980. After graduating from Stanford in 1984, he returned to South Africa for a year with his earlier employer SA Medical Research Council. He returned to the USA in March 1986 and joined the statistics and data analysis research group at what was then AT&T Bell Laboratories in Murray Hill, New Jersey. After eight years at Bell Labs, he returned to Stanford University in 1994 as Professor in Statistics and Biostatistics. In 2013 he was named the John A. Overdeck Professor of Mathematical Sciences.
His main research contributions have been in applied statistics, and
he has written three books in this area: “Generalized Additive Models” (with R. Tibshirani, Chapman and Hall, 1991), “Elements of Statistical Learning” (with R. Tibshirani and J. Friedman, Springer 2001; second edition 2009) and “An Introduction to Statistical Learning, with Applications in R” (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 of modeling tools in the S language (Statistical Models in S, Wadsworth, 1992), which form the basis for much of the statistical modeling in R. His current research focuses on applied statistical modeling and prediction problems in biology and genomics, medicine and industry.
