Nancy Reid is Professor of Statistical Sciences at the University of Toronto. She is also serving as Canada Research Chair in Statistical Theory and Applications and recently completed the role of Scientific Director of the Canadian Statistical Sciences Institute. Professor Reid is a Fellow of the Royal Society, the Royal Society of Canada, the Royal Society of Edinburgh, the American Association for the Advancement of Science, and a Foreign Associate of the National Academy of Sciences. In 2015 she was appointed Officer of the Order of Canada. She has been recognized with the COPSS Presidents' Award, the Florence Nightingale David Award, the Parzen Prize for Statistical Innovation, and the SSC Gold Medal.
Nancy's main area of research is theoretical statistics, being interested in the foundations and properties of inference methods. Much of her work is self-described as considering how best to ensure that inferential statements about quantities of interest are both accurate and effective at summarizing complex data sets. Her published books include Theory of the Design of Experiments with David Cox and Applied Asymptotics with Alessandra Brazzale and Anthony Davison, which aims to illustrate the application of this theoretical work on models useful for applied statistics.
Arriving from her master's work at the University of British Columbia, Nancy earned her PhD from Stanford Statistics in 1979 under Ruper Miller with Brad Efron and Vernon Johns also on the committee:
Rupert G. Miller was Brad Efron's supervisor. He was a very kind man with a great sense of humor. In the first year at Stanford, you had to take Math Stat, Probability, and Applied Statistics, and then write exams on them. Rupert taught Applied Statistics; the notes formed the basis for his book, Beyond ANOVA: Basics of Applied Statistics (1986). Everyone at UBC had told me that I should work with Brad, so I went to ask him if I could work with him. And he said that he wouldn't be a very good supervisor, because if he was interested in a problem, then he would figure it out himself, and if he wasn't interested in the problem, he didn't want to meet and talk about it every week. He encouraged me to talk to Rupert, who had a little drawer in his desk with a stack of file cards, and on each file card was written a problem. So he just opened the drawer and started looking through. Then, he handed me this file card that had a problem on it about robust inference for survival data. And he told me how to start as well, because he had a student working on expressing the survival data estimator as a functional, which was the first step in getting into robustness. So that's what I worked on.