Doug Wiens is a professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He was born in Lloydminster, Saskatchewan in 1950 and grew up in Calgary. He received his BSc in Mathematics (1972), two Masters degrees - in Mathematical Logic (1974) and in Statistics (1979), and a PhD in Statistics (1982), all from the University of Calgary. As part of his work on mathematical logic, Doug helped find a Diophantine formula for the primes in connection with Hilbert’s tenth problem: that is, a multivariate polynomial with the property that the positive values of this polynomial, over integer arguments, are exactly the prime numbers. Doug and his co-authors won the Lester R. Ford Award of the Mathematical Association of America in 1977 for their paper on this result. His PhD dissertation was entitled “Robust Estimation for Multivariate Location and Scale in the Presence of Asymmetry” and was supervised by John R. Collins. After receiving his PhD in 1982 Doug took a faculty position at Dalhousie University before moving to the University of Alberta in 1987. 
                                                             

Doug is a world leader in the areas of robust statistics and experiment design, and works at the interface of these disciplines. He and his coauthors have continued and extended work initiated by Box and Draper (1959) and Huber (1975) on robustness of design. The ‘classical’ optimal designs typically depend strongly on strict model assumptions for their optimality. However the true model will generally depart slightly from the experimenter’s assumed model. Doug’s research provides designs with high efficiency when the model is correct, but which also maintain efficiency under deviations from the model.
 

Doug’s work is technically challenging and of both theoretical and practical importance. He has developed criteria yielding optimally robust (‘minimax’) designs for many practical settings, found mathematical bounds for the criteria, and explored methods for computing the designs. Some critically important contributions in design include (i) minimax designs for prediction in approximately linear regression models; (ii) minimax robust designs for estimation and extrapolation in heteroscedastic, approximately linear models; (iii) robust designs to discriminate between nonlinear, approximate regression responses; (iv) robustness of design in dose-response studies when the link function is perhaps incorrectly specified; and (v) model-robust designs for quantile regression.
 

Before applying concepts of robustness to design, Doug initiated a study of minimax variance M-estimation of multivariate location and scale and of robust L- and R- estimators of location, for which he received The Canadian Journal of Statistics Award in 1990. More recent work has included the derivation of efficient algorithms for the computation of robust designs, and the extension of concepts of design to model-based sampling and to robust active learning.
 

Doug has contributed enormously to the training of highly qualified personnel, as supervisor of four postdoctoral fellows and as thesis supervisor of eight PhD students and of 27 MSc students. These students are active researchers themselves and have published papers jointly with Doug in high quality journals. Most of his former PhD students currently hold professorships at universities in Canada and abroad. Through his teaching Doug has also been a terrific mentor of young researchers.
 

Doug has served the statistics community nationally and internationally. He has served, and continues to serve, on various editorial boards. He has been Editor of The Canadian Journal of Statistics (2001-2003), and has served as associate editor and guest editor for numerous top statistical journals. He has served on NSERC Grant Selection Committees and on numerous SSC committees, and has participated in organizing conferences and workshops. His contributions to research and to the statistics community led to his election in 2005 to Fellow of the American Statistical Association.