This event is organized by the Ontario Regional Centre of the Canadian Statistical Sciences Institute (CANSSI Ontario).
Note: Event details can change. Please visit the unit’s website for the latest information about this event.
Annual Distinguished Lecture in Statistical Sciences with Daniela Witten
CANSSI Ontario is proud to host this year’s Distinguished Lecture in Statistical Sciences with:
Dr. Daniela Witten
Professor of Statistics & Biostatistics
Dorothy Gilford Endowed Chair of Mathematical Statistics
Departments of Statistics and of Biostatistics
University of Washington
Dates: October 1 and 2, 2026
Format: Free Hybrid Event (In person in Toronto / Live by Zoom)
Registration required: https://survey.alchemer-ca.com/s3/50614304/DLSS-Daniela-Witten-Registration-Form
Location: 10th Floor, 700 University Avenue, Toronto, ON. Please arrive early, as seating is limited.
A reception follows the lecture.
Oct 1 | 3:30–4:30 p.m. ET
Talk Title: Selective Inference and its Applications
Abstract: Classical statistics centres around the setting where the questions that we ask about our data are specified in advance, before we even look at our data. However, in contemporary settings, often the data analyst comes up with a question only after looking at the data. Classical methods break down in this setting: for instance, standard hypothesis tests do not achieve type 1 error control, and confidence intervals do not attain the nominal coverage. In this overview talk, I will introduce the field of selective inference, which encompasses a number of recent approaches to conduct inference on hypotheses or parameters that are selected from the data, rather than specified in advance.
Speaker Profile: Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair in Mathematical Statistics. She develops statistical machine learning methods for high-dimensional data, with a focus on unsupervised learning.
She has received a number of awards for her research in statistical machine learning: most notably the Spiegelman Award from the American Public Health Association for a (bio)statistician under age 40, and the Presidents’ Award from the Committee of Presidents of Statistical Societies for a statistician under age 41.
Daniela is a co-author of the textbook “Introduction to Statistical Learning”, and has served as Joint Editor of Journal of the Royal Statistical Society, Series B.