Workshop: Statistical Foundations of AI

Events

Author

Shan Gao

Published

February 2, 2026

2nd Workshop: Uncertainty in AI | IVADO

IVADO Workshop

2nd Workshop: Uncertainty in AI

A four-day gathering focused on the statistical foundations of uncertainty in modern AI, from predictive reliability and generative AI hallucinations to theory-driven advances in deep learning and transfer learning.

Date
June 8–11, 2026

Time
9:30 AM–5:00 PM (America/Toronto)

Location
IVADO, 950 Av. Beaumont, Suite #201, Montréal

Fee
$70–$280 (before tax)

Official Event Page Thematic Program

What to expect

As AI systems move into high-stakes domains, understanding uncertainty is essential for reliability and trust. This workshop highlights principled statistical approaches that clarify how and why AI systems can fail—and how to quantify those risks rigorously.

Predictive uncertainty

Generative AI hallucinations

Bayesian deep learning

Transformers & interacting particle systems

Transfer learning & optimal transport

Theory of chain-of-thought reasoning

Organizing committee

Eric Kolaczyk
McGill University

Qiang Sun
Toronto University

Confirmed speakers

Ben Adcock (SFU)
Chris Maddison (University of Toronto)
Claire Donnat (University of Chicago)
Eric Moulines (Ecole Polytechnique, MBZUAI)
Geoff Pleiss (UBC)
Kostas Spiliopoulios (Boston University)
Kun Zhang (CMU, MBZUAI)
Maxim Panov (MBZUAI)
Mehdi Dagdoug (McGill University)
Mladen Kolar (USC)
Murat A. Erdogdu (University of Toronto)
Phillipe Rigollet (MIT)
Qiang Liu (UT Austin)
Rachel Morris (Concordia University)
Tianxi Cai (Harvard)
Xin Bing (University of Toronto)
Xinwei Shen (UW Seattle)
Zhenyu Liao (HUST)
Fanny Yang (ETH)

Registration

Registration opens February 2, 2026. Fees are listed at $70–$280 per participant category. The workshop is part of IVADO’s Statistical Foundations of AI thematic semester.