2025
Student Seminar: Rahul Kukreja
9:30–10:00 am Jones 111
Thursday, November 13, 2025, at 9:30 AM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis l Presentation
Rahul Kukreja, Department of Statistics, The University of Chicago
“Interpreting Transformers for Time-Series Forecasting”
Student Seminar: Aditi Gupta
3:00–3:30 pm Jones 111
Wednesday, November 12, 2025, at 10:00 AM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis l Presentation
Aditi Gupta, Department of Statistics, The University of Chicago
“Sparse Inverse–Cholesky Estimation under Model Misspecification”
Student Seminar: Sulagna Ghosh
10:00–11:30 am Jones 111
Wednesday, November 12, 2025, at 10:00 AM, in Jones 111, 5747 S. Ellis Avenue
Dissertation Proposal Presentation
Sulagna Ghosh, Department of Statistics, The University of Chicago
“Scalable gradient-based variational inference for flexible point process modeling of spatiotemporal data”
Student Seminar: Xinyue Lou
3:30–4:00 pm Jones 111
Tuesday, November 11, 2025, at 3:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis l Presentation
Xinyue Lou, Department of Statistics, The University of Chicago
“Low-Rank Reinforcement Learning for Robust Value Estimation”
Student Seminar: Yuetong Cathy Li
4:00–4:30 pm Jones 111
Monday, November 10, 2025, at 4:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis l Presentation
Yuetong Cathy Li, Department of Statistics, The University of Chicago
“NaCLO: A Benchmark for Evaluating Reasoning in Large Language Models via Low-Resource Linguistic Puzzles”
Statistics Colloquium: Tim Armstrong
11:30 am–12:30 pm Jones 303
Timothy Armstrong, Department of Economics, University of Southern California
Title: “Asymptotic Efficiency Bounds for a Class of Experimental Designs”
Abstract:
We consider an experimental design setting in which units are assigned to treatment after being sampled sequentially from an infinite population. We derive asymptotic efficiency bounds that apply to data from any experiment that assigns treatment as a (possibly randomized) function of covariates and past outcome data, including stratification on covariates and adaptive designs. For estimating the average treatment effect of a binary treatment, our results show that no further first order asymptotic efficiency improvement is possible relative to an estimator that achieves the Hahn (1998) bound in an experimental design where the propensity score is chosen to minimize this bound. Our results also apply to settings with multiple treatments with possible constraints on treatment, as well as covariate based sampling of a single outcome.
Student Seminar: Paris Hsu
3:00–3:30 pm Jones 111
Wednesday, November 5, 2025, at 3:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis l Presentation
Paris Hsu, Department of Statistics, The University of Chicago
“Geographic Revenue Factor: High-Dimensional Alpha Test & ETF Design”
Statistics Colloquium: Chiara Sabatti
11:30 am–12:30 pm Jones 303
Chiara Sabatti, Department of Biomedical Data Science and Statistics, Stanford University
Title: “Searching for local associations while controlling the false discovery rate”
Abstract: In this talk I will describe local conditional hypotheses that express how the relation between explanatory variables and outcomes changes across different contexts, described by covariates. I will then introduce efficient testing strategies for these hypotheses.
The motivation for this work comes from genetics and genomics. For example, as the evidence obtained from genome-wide association studies accumulates, it has become apparent that some genetic variants carry information on phenotypes in some populations and not in others. There are multiple explanations contributing to this phenomenon. Among others, it is possible that some genetic variations might be relevant for the trait of interest only in specific environmental conditions, the exposure to which varies across human populations.
To identify the combination of explanatory variables and covariates that influence an outcome, we build upon the knockoff framework for FDR control and powerful pre-screening strategies. Specifically, the method we propose can leverage any model for the identification of data-driven hypotheses pertaining to different contexts. Then it rigorously tests these hypotheses without succumbing to selection bias. The approach is efficient and does not require sample splitting. We demonstrate the effectiveness of our method through numerical experiments and by studying the genetic architecture of waist/hip ratio across different sexes in the UK Biobank.
This work is in collaboration with Matteo Sesia and Paula Gablenz.
Student Seminar: Yeo Jin Jung
10:00 am–12:00 pm Jones 111
Tuesday, October 28, 2025, at 10:00 AM, in Jones 111, 5747 S. Ellis Avenue
Dissertation Proposal Presentation
Yeo Jin Jung, Department of Statistics, The University of Chicago
“Structured Representation Learning in High-dimensional Data”
Student Seminar: Yiheng Yang
3:30–4:00 pm Jones 111
Monday, October 27, 2025, at 3:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis l Presentation
Yiheng Yang, Department of Statistics, The University of Chicago
“Sensitivity Analysis for Permutations of Hidden Biases in Matched Observational Studies”