CANSSI SSC and 2026 Van Eeden Seminar: Online Conformal Prediction, Multi-Level Quantile Tracking, and Gradient Equilibrium
Date: Thursday, April 2, 2026
Time: 10:30–12:00 Pacific time
Location: Online or ESB 5104/5106 at the University of British Columbia
Join Us
This special event represents a convergence of the CANSSI SSC Seminar on Innovations in Statistics and Data Science and the Constance van Eeden Seminar, an annual event held at the University of British Columbia.
The CANSSI SSC Seminar is a new series co-sponsored by CANSSI and the Statistical Society of Canada (SSC) that brings distinguished researchers in statistical sciences to CANSSI member universities across Canada. The series promotes interactions between leading researchers and statistical sciences faculty members and students, particularly at smaller institutions.
The Constance van Eeden seminar is a yearly event in which graduate students from the University of British Columbia (UBC)’s Department of Statistics vote for their favourite statisticians. The winner is contacted by the organizing committee and invited to give a talk in the department’s seminar. The speaker spends one or two days on campus, and graduate students have the opportunity to have lunch and dinner with them.
Registration
To register for online or in-person participation, visit the event web page.
About This Year’s Speaker
Dr. Ryan Tibshirani has been invited to be this year’s van Eeden speaker by the graduate students in the Department of Statistics at the University of British Columbia. A van Eeden speaker is a prominent statistician who is chosen each year to give a lecture, supported by the UBC Constance van Eeden Fund. The 2026 seminar is additionally sponsored by the Canadian Statistical Sciences Institute (CANSSI), the Pacific Institute for the Mathematical Sciences (PIMS), and the Walter H. Gage Memorial Fund.
Presentation Abstract
This talk is about uncertainty quantification for time series prediction.
The overarching goal is to provide easy-to-use algorithms with formal guarantees. The algorithms we present build upon ideas from conformal prediction and control theory, are able to prospectively model conformal scores in an online setting, and adapt to the presence of systematic errors due to seasonality, trends, and general distribution shifts. We will then discuss an extension of these ideas to the setting of probabilistic forecasting, which is essentially
a generalization of the framework to handle vector-valued predictions, i.e., predictions which take the form of a set of ordered quantile forecasts at different probability levels. Finally, we will generalize this even further to discuss an abstract property in online learning called gradient equilibrium, which encapsulates these settings, and more.