October 28-29, 2025–
JW Reitz Union, Room G320–
4:00pm to 5:00pm
Nancy Reid
Nancy Reid studied at the University of Waterloo (B.Math. 1974), the University of British Columbia (M.Sc. 1976), Stanford University (PhD 1979) and Imperial College, London (PDF 1980). She joined the University of Toronto in 1986 from the University of British Columbia. She has held several leadership roles in statistical science including Chair of the Department (1997–2002) and Scientific Director of the Canadian Statistical Sciences Institute (2015–2019). Nancy is a Fellow of the Royal Society, the Royal Society of Canada, the Royal Society of Edinburgh, and a Foreign Associate of the National Academy of Sciences. In 2015 she was appointed Officer of the Order of Canada.
General Lecture
Tuesday, October 28, 2025, 4:00PM to 5:00PM
Title:
Lies, Damned Lies, and Statistics
Abstract:
This is the title I used the first time I taught the U of T First-Year Seminar course, many years ago now. I was nervous about the prospect of a seminar-style course for students just out of high school. As it turned out, however, this course had a big, positive, impact on my teaching, my research, and my ‘world-view’ of statistical science. Although much has changed in our field in the years since, the basic principles of reasoning with uncertainty have not. In this talk I will reflect on my experiences in trying to convey the ongoing importance of statistical science and perhaps hazard a guess about the future.
Technical Lecture
Wednesday, October 29, 2025, 4:00PM to 5:00PM
Title:
Models and Likelihood
Abstract:
With vast amounts of data available in nearly every scientific endeavor, parametrized models may seem an anachronism from a more classical age of statistics. However, the likelihood function, or one of its many variants, is regularly used in both theory and applications as a starting point, or a useful tool, or even a gold standard. This talk will discuss likelihood functions with high-dimensional parameters and the connections to nonparametric and semi-parametric modelling, with an emphasis on the importance of foundational thinking in successful statistical practice.