2026
DSI/Statistics Colloquium: Frederic Koehler
11:30 am–12:30 pm DSI 105
Frederic Koehler
DSI/Department of Statistics
University of Chicago
Title: A Least-Squares Perspective on Ellipsoid Fitting
Abstract: Suppose we have n i.i.d. random vectors X_1,\ldots, X_n in d dimensions. How large can n be, as a function of d, such that there exists an ellipsoid interpolating all n points? Saunderson, Parrilo, and Willsky conjectured an explicit answer (n ~ d^2/4) for Gaussian data. Subsequently, many authors made rigorous progress on this question. We study a natural least-squares generalization of ellipsoid fitting, over a general class of data distributions, and solve its high-dimensional limit—- this proves the Gaussian conjecture as a special case. The solution combines in a nice way ideas from high-dimensional probability, statistical learning, and convex geometry + optimization.
Based on a joint work with Youngtak Sohn.
DSI/Statistics Colloquium: Nikolaos Ignatiadis
11:30 am–12:30 pm DSI 105
Nikolaos Ignatiadis
DSI/Department of Statistics
University of Chicago
Title: Borrowing Strength in Multiple Testing with Compound p-values
Abstract: Many large-scale multiple testing problems in high-throughput biology involve thousands of units (e.g., genes or proteins) but only a small number of replicates per unit. A key observation in the microarray literature around the turn of the century was that standard t-tests may be nearly powerless in this setting. This motivated the development of methods such as the B-statistic (Lönnstedt and Speed, 2002), Significance Analysis of Microarrays (SAM; Tusher, Tibshirani and Chu, 2001), and limma (Smyth, 2004), which borrow information across units.
We study this type of borrowing across units through the lens of compound p-values. Unlike bona fide p-values, compound p-values need not be valid for each hypothesis separately, but instead satisfy an average superuniformity property across the null hypotheses. We first explain how compound p-values arise both from a nonparametric version of limma and from SAM. We then use the compound p-value perspective to construct a procedure that achieves the asymptotic power of the oracle Bayes rule in a sparse regime under the hierarchical model underlying the B-statistic and provides finite-sample frequentist control of the false discovery rate without requiring this hierarchical model to hold.
Statistics Colloquium: Xinran Li
11:30 am–12:30 pm Jones 303
Xinran Li
Department of Statistics
University of Chicago
Title: Randomization Inference with Sample Attrition
Abstract: Sample attrition, or equivalently missing outcomes, is a common challenge in randomized controlled experiments, and analyses that simply discard units with missing outcomes may suffer from severe bias. In this talk, we develop computationally efficient methods for randomization inference that remain valid under a broad class of potentially informative missingness mechanisms, allowing a unit’s missingness to depend on its potential outcomes. We pay particular attention to monotone missingness mechanisms, under which outcomes are systematically more likely to be observed under treatment than under control, or vice versa. Such restrictions arise naturally in many empirical settings, especially in the social sciences.
Specifically, we construct valid p-values for testing sharp, bounded, and quantile null hypotheses on treatment effects through a worst-case consideration of the classical Fisher randomization test. By leveraging distribution-free test statistics, these worst-case p-values can be computed efficiently and often admit closed-form solutions. Importantly, by incorporating both potential outcomes and potential missingness indicators into the test statistic, our methods can exploit structural assumptions such as monotone missingness to sharpen inference. Our approach also connects to a range of partial-identification bounds in the literature. We illustrate the proposed methods through simulation studies and empirical applications evaluating the impacts of job training, media censorship, and artificial intelligence.
Student Seminar: Yinjie Wang
4:30–5:00 pm Jones 111
Thursday, July 23, 2026, at 4:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Yinjie Wang, Department of Statistics, The University of Chicago
“Simple and Effective Query-Adaptive Sparse Attention”
Student Seminar: Arnav Rastogi
2:00–2:30 pm Jones 111
Thursday, July 23, 2026, at 2:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Arnav Rastogi, Department of Statistics, The University of Chicago
“Measuring, Representing, and Steering Premium–Value Brand Positioning in Beauty and Personal Care”
Student Seminar: Brian Park
10:00–10:30 am Jones 111
Wednesday, July 22, 2026, at 10:00 AM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Brian Park, Department of Statistics, The University of Chicago
“Separating Predictive Gains from Incremental Information: Text Embeddings and Analyst Forecasts of Corporate Earnings”
Student Seminar: Yuta Iwai
2:30–3:00 pm Jones 111
Monday, July 20, 2026, at 2:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Yuta Iwai, Department of Statistics, The University of Chicago
“Evaluation of McScan for Change Point Detection in High-Dimensional Linear Regression with an Application to Macroeconomic Data”
Student Seminar: Pratik Chheda
2:00–2:30 pm Jones 111
Monday, July 20, 2026, at 2:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Pratik Chheda, Department of Statistics, The University of Chicago
“Comparing Multivariate Value at Risk Forecasting Models: A Reproduction and Empirical Extension”
Student Seminars: Ziming Gan
10:00–11:00 am Zoom Meeting
Wednesday, July 8, 2026, at 10:00 AM, in Jones 111, 5747 S. Ellis Avenue
Dissertation Defense Presentation
Ziming Gan, Department of Statistics, The University of Chicago
“Statistical Methods for Structure Discovery in Genomic and Clinical Data”
Student Seminars: Kulunu Dharmakeerthi
12:30–2:00 pm Jones 111
Thursday, June 18, 2026, at 12:30 PM, in Jones 111, 5747 S. Ellis Avenue
Dissertation Defense Presentation
Kulunu Dharmakeerthi, Department of Statistics, The University of Chicago
“Recovering Structure from Observational Data: Representation, Simulation and Stability”