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Brad is Max H. Stein Professor of Humanities and Sciences, Professor of Statistics, and Professor of Biostatistics with the Department of Biomedical Data Science in the School of Medicine; he serves as Co-director of the Mathematical and Computational Sciences Program. He has held visiting faculty appointments at Harvard, UC Berkeley, and Imperial College, London. A recipient of a 2005 National Medal of Science for his contributions to theoretical and applied statistics, especially the bootstrap sampling technique, in 2014 he was awarded the Guy Medal in Gold by the Royal Statistical Society. Together with David Cox of the University of Oxford, Efron was honored as a Laureate in the 2016 edition of the BBVA Foundation Frontiers of Knowledge Awards, “for revolutionizing statistics and making it into an indispensable tool for other sciences.”
My main project during the last two years has been "Computer Age Statistical Inference", a book written in collaboration with Trevor Hastie. Our goal is to review the development of statistical thinking since the introduction of electronic computation in the 1950s. Individual chapters concern progress in important topic areas, emphasizing the interplay between computational methods and inferential ideas. The survival analysis chapter, for example, traces the steps from life tables, the Kaplan-Meier estimator, and the Mantel Haenszel log rank test to the proportional hazards model.The book proceeds in three parts: Part 1 reviews classical inference, Bayesian, frequentist, and Fisherian. Part 2 covers developments from 1955 through 1995, empirical Bayes, James-Stein estimation, ridge regression, generalized linear models, jackknife and bootstrap methods, objective Bayes and MCMC, plus several other topics. Part 3 concerns 21st century topics, false discovery rates, sparse modeling and the lasso, support vector machines, neural networks, random forests, and other modern data analytic algorithms. First drafts of Parts 1 and 2 are now complete. Part 3 is in progress, and we hope to complete work in 2016.The broader impact of my other work over the previous few years has been to establish both within and outside the field the practical importance of computer-intensive statistical methods, such as the bootstrap, shrinkage estimation, and local false discovery rates. Besides gaining traction themselves, these methods have stimulated parallel developments in other areas, such as MCMC algorithms for Bayesian calculations.