Hank Suz-Chi Wan Memorial Lecture - David Blei
October 6, 2026 4:30PM—6:00PM
Location:
In Person
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Rashid Auditorium, Gates Hillman 4401
Speaker:
DAVID BLEI,
William B. Ransford Professor of Statistics and Computer Science, Departments of Statistics and Computer Science, Columbia University
https://www.cs.columbia.edu/~blei/
Empirical Bayes improves simultaneous inference by learning from related data. In this talk, I will present three recent directions in empirical Bayes. First, I will discuss an empirical Bayes approach to combining randomized experiments and observational studies, where calibration studies make it possible to learn the distribution of observational bias and use observational data in a principled way. Second, I will discuss empirical Bayes for implicit likelihoods, where the model is available only through a simulator, and show how simulation-based inference can be used to produce empirical Bayes estimates without evaluating a density. Third, I will discuss a general method based on probabilistic symmetries, which extends empirical Bayes beyond exchangeable settings to structured problems such as arrays, graphs, conditional data, and spatial models. These three ideas illustrate new roles for empirical Bayes in modern statistics and machine learning.
This is joint work with Diana Cai, Don Green, Sebastian Salazar, Xinwei Shen, Eli Weinstein, Bohan Wu, Cheng Zhang.
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David Blei is the William B. Ransford Professor of Statistics and Computer Science at Columbia University. He studies probabilistic machine learning and Bayesian statistics, including theory, algorithms, and application. David has received several awards for his research, including the ACM Prize in Computing (2013), a Guggenheim fellowship (2017), a Simons Investigator Award (2019), the AAAI John McCarthy award (2024), and the ACM/AAAI Allan Newell Award (2024). He was the co-editor-in-chief of the Journal of Machine Learning Research from 2019-2024. He is a fellow of the Association for Computing Machinery (ACM) and the Institute of Mathematical Statistics (IMS).
Faculty Host: Aarti Singh
Part of the Kenneth C. Griffin School of Computer Science Distinguished Lecture Series
About the Lecture: The Hank Suz-Chi Wan Memorial Lecture, an annual distinguished lecture in the School of Computer Science, was established in 1996 in loving tribute by the family, friends, and colleagues of beloved CS Ph.D. student, Hank Suz-Chi Wan. The lecture reflects his belief in "the powers of computer science to create a better world" and his endless amazement and faith in those leading the sciences comprising this vast field.
For More Information:
scs-dls@cs.cmu.edu