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 2 | 1:15–2:15 p.m. ET
Talk Title: Testing Hypothesis via Orthogonalization
Abstract: Classical hypothesis testing frameworks break down in contemporary settings in which null hypotheses are increasingly abstract, the same data are used to both generate and test hypotheses, and minimal assumptions about the underlying data are made. In this work, we propose a new framework for conducting valid hypothesis tests in broad contexts. We propose to add and subtract external noise generated from a symmetric shift-family to our data, X, to partition it into two pieces, X1 and X2. We provide a generic strategy for orthogonalizing X2 against X1 under the null hypothesis H0, then show that testing whether the orthogonalization was successful provides a valid test of H0 under mild assumptions. Remarkably, this framework extends naturally to the post-selection inference setting with minimal modifications: we simply select a hypothesis on X1, then perform orthogonalization under the selected null. As our approach neither requires pre-specification of the selection mechanism, nor is restricted to a small class of data-generating distributions, it dramatically expands the settings for which valid post-selection inference can be conducted. We showcase the flexibility of our proposal in a number of case studies. This is joint work with Ameer Dharamshi (McGill University) and Runjia Zou (University of Washington).
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.