2026
Statistics Colloquium: David Blei
11:30 am–12:30 pm Jones 303
David Blei
Departments of Statistics and Computer Science
Columbia University
Title: A Fresh Look at Empirical Bayes
Abstract: 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 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. 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 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. 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 Sebastian Wagner-Carena, Bohan Wu, Cheng Zhang.
Student Seminar: Tommaso Castellani
2:30–3:00 pm Jones 111
Monday, May 11, 2026, at 2:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Tommaso Castellani, Department of Statistics, The University of Chicago
“Bias, Measurement Error, and Double-Dipping: When Can GNN Convolutions Help Brain Connectome Prediction?”
Statistics Colloquium: Aravindan Vijayaraghavan
11:30 am–12:30 pm Jones 303
Aravindan Vijayaraghavan
Department of Computer Science
Northwestern University
Title: Finding Small Confidence Sets in High Dimensions
Abstract: Constructing confidence sets is a fundamental problem in statistics: given samples from an arbitrary distribution and a target coverage $1-\alpha$ (e.g., 0.90), the goal is to find a set that covers $1-\alpha$ probability mass while having as small a volume as possible. This task underlies a wide range of applications, including uncertainty quantification and support estimation. Even when restricted to simple geometric families such as Euclidean balls, finding small confidence sets is computationally challenging in high dimensions.
This raises a key question: can we design computationally efficient methods that find these sets with provably near-optimal size?
In this talk, I will present new algorithms that learn confidence balls and confidence ellipsoids with rigorous guarantees of coverage and approximate volume optimality. The algorithms use new connections to robust statistics, convex optimization duality, and the Brascamp-Lieb inequality. Time permitting, I will discuss discrete variants of the problem and their applications to conformal prediction.
Based mostly on joint work with Chao Gao, Liren Shan, and Vaidehi Srinivas.
Student Seminar: Zijiang Yang
4:30–5:00 pm Jones 111
Thursday, May 7, 2026, at 4:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Zijiang Yang, Department of Statistics, The University of Chicago
“Contrastive Representation Learning for Prompt–Response Alignment”
Student Seminars: Yuwei Cheng
2:30–4:00 pm Jones 111
Thursday, May 7, 2026, at 2:30 PM, Jones 111, 5747 S. Ellis Ave.
Dissertation Defense Presentation
Yuwei Cheng, Department of Statistics, The University of Chicago
“Aligning Machine Learning Systems with Human Preferences: Robustness, Personalization, and Evaluation”
Student Seminar: Xueran Tao
2:00–2:30 pm Jones 111
Thursday, May 7, 2026, at 2:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Xueran Tao, Department of Statistics, The University of Chicago
“Finite-Sample Analysis of Prediction-Powered Inference for Linear Regression”
Student Seminar: Tianze Deng
1:30–2:00 pm Jones 111
Thursday, May 7, 2026, at 1:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Tianze Deng, Department of Statistics, The University of Chicago
“Queueing System Design with General Customer Abandonment: Priority Queues versus Dedicated Queues in Moderate Overload”
Student Seminar: Yuhan Wang
1:00–1:30 pm Jones 111
Thursday, May 7, 2026, at 1:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Yuhan Wang, Department of Statistics, The University of Chicago
“Ablation and Architectural Extensions of STAMP for Spatial Transcriptomics”
Student Seminar: Pippa Lin
12:00–12:30 pm Jones 111
Thursday, May 7, 2026, at 12:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Pippa Lin, Department of Statistics, The University of Chicago
“Linear Directions of Political Ideology in LLMs: Probing, Generalization, and Intervention”