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E. Bura, D. Pena, L. Li, C. J. Nachtsheim, C. Setodji, R. E. Weiss, B. Li:
"A Conversation with Dennis Cook";
Statistical Science, 36 (2021), 2; 328 - 337.

Dennis Cook is Full Professor, School of Statistics, University of Minnesota. He received his BS degree in Mathematics from Northern Montana College, and MS and PhD degrees in statistics from Kansas State University. He has served as Chair of the Department of Applied Statistics, Director of the Statistical Center and Director of the School of Statistics, all at the University of Minnesota.
His research areas include dimension reduction, linear and nonlinear regression, ex- perimental design, statistical diagnostics, statistical graphics and population genetics. He has authored over 200 research articles and is author or co-author of two textbooks - An Introduction to Regression Graphics, and Applied Regression Including Computing and Graphics - and three research monographs, Influence and Residuals in Regression, Regression Graphics: Ideas for Studying Regressions through Graphics and An Intro- duction to Envelopes: Dimension Reduction for Efficient Estimation in Multivariate Statistics.
He has served as Associate Editor of the Journal of the American Statistical Associ- ation, The Journal of Quality Technology, Biometrika, Journal of the Royal Statistical Society and Statistica Sinica. He is a four-time recipient of the Jack Youden Prize for Best Expository Paper in Technometrics as well as the Frank Wilcoxon Award for Best Technical Paper. He received the 2005 COPSS Fisher Lecture and Award, and he is a Fellow of the American Statistical Association and the Institute of Mathematical Statis- tics. The following conversation took place on March 22, 2019, following the banquetat the conference, "Cook´s Distance and Beyond: A Conference Celebrating the Con- tributions of R. Dennis Cook." The interviewers were, Efstathia Bura (Effie), Daniel Pena, Lexin Li, Christopher Nachtsheim (Chris), Claude Messan Setodji, Robert Weiss (Rob), and Bing Li.

Bayesian methods, central subspace, Cook´s distance, envelope methods, influence, neural networks, optimal experimental design, regression diagnostics, sufficient dimension reduction

http://dx.doi.org/10.1214/20-STS801