Dalla Lana School of Public Health

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CANSSI Ontario is proud to host this year’s Distinguished Lecture in Statistical Sciences with:

Daniela Witten
Professor of Statistics & Biostatistics
Dorothy Gilford Endowed Chair of Mathematical Statistics
University of Washington

Dates: October 1 and 2, 2026
Format: Hybrid Event (In person in Toronto / Online by Zoom)
Free event, registration required
Location: 10th Floor, 700 University Avenue, Toronto, ON.
Please arrive early, as seating is limited.
A reception follows the lecture on October 1.

Oct 1 | 3:30–4:30 p.m. ET
Talk Title: Selective Inference and its Applications

Abstract: Classical statistics centers 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.

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.