2025
Student Seminar: Melissa Adrian
10:00 am–12:00 pm Jones 303
Thursday, June 26, 2025, at 10:00 AM, in Jones 303, 5747 S. Ellis Avenue
PhD Dissertation Defense Presentation
Melissa Adrian, Department of Statistics, The University of Chicago
“Machine learning in large-scale data assimilation and stable model selection”
Student Seminars: Wentao Hu
9:30–11:00 am Jones 304
Wednesday, June 11, 2025, at 9:30 AM, in Jones 304, 5747 S. Ellis Avenue
PhD Dissertation Defense Presentation
Wentao Hu, Department of Statistics, The University of Chicago
“Inference for Time Series in Risk Measure and Regression”
Student Seminar: Yu Sen Lui
4:00–4:30 pm Jones 304
Thursday, June 5, 2025, at 4:00 PM, in Jones 304, 5747 S. Ellis Avenue
Master’s Thesis l Presentation
Yu Sen Lui, Department of Statistics, The University of Chicago
“Comparing machine-coded international relations dyadic data with Bayesian nonnegative tensor factorisation”
Student Seminar: Andrew Zhang
4:00–4:30 pm Jones 111
Wednesday, May 28, 2025, at 4:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis l Presentation
Andrew Zhang, Department of Statistics, The University of Chicago
“Benchmarking Six Graph Neural Network Methods for Spatial Domain Detection”
Statistics Colloquium: Michael Hudgens
11:30 am–12:30 pm Jones 303
Michael Hudgens, Department of Biostatistics, University of North Carolina at Chapel Hill
Title: Causal Inference with Interference
Abstract: A fundamental assumption usually made in causal inference is that of no interference between individuals (or units), i.e., the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in infectious diseases, whether one person becomes infected may depend on who else in the population is vaccinated. In this talk we will discuss recent approaches to assessing treatment effects in the presence of interference.
2nd Year Mini Seminars
1:00–4:00 pm Jones 303
Friday, May 16, 2025, from 1:00-4:30 PM, in Jones 303, 5747 S. Ellis Avenue
Mini-Seminars for Second-Year PhDs
Moderator: Jimmy Lederman
1:00 pm: Beining Wu, Can Neural Networks Achieve Optimal Computational-statistical Gaps? An Analysis on Single-index Models
1:15 pm: Minjun Cho, Adaptive Robust Confidence Intervals for the Binomial Distribution
1:30 pm: Woohyun Choi, Empirical Bayes Mean Estimation via Summary Statistics under Nonparametric Errors
1:45 pm: Ankur Garg, Learning Discrete Causal Representations from Heterogeneous Domains: A Bayesian Approach
2:00 pm: Junming Guan, Empirical Bayes Principal Component Analysis for Automatic Dimensionality Selection
2:15 pm: Qiyuan Liu, Using gRNA Efficiency to Enhance Detection Power in Perturb-seq
2:30 pm: Tannistha Mondal, Practical Sparse Generalized Canonical Correlation Analysis
2:45 pm: David Chen, Yet Another Conditional Independence Test
3:00 pm: Zixuan Wu, Link Prediction using Graph Neural Networks
3:15 pm: Dong Xie, Adaptive confidence interval in robust regression
Statistics Colloquium: Anne van Delft
11:30 am–12:30 pm Jones 303
Anne van Delft, Department of Statistics, Columbia University
Title: A statistical framework for analyzing shape in a time series of random geometric objects (joint work with Andrew J. Blumberg)
Abstract: We introduce a new framework to analyze shape descriptors that capture the geometric features of an ensemble of point clouds. At the core of our approach is the point of view that the data arises as sampled recordings from a metric space-valued stochastic process, possibly ofnonstationary nature, thereby integrating geometric data analysis intothe realm of functional time series analysis. Our framework allows for natural incorporation of spatial-temporal dynamics, heterogeneous sampling, and the study of convergence rates. Further, we derive complete invariants for classes of metric space-valued stochastic processes in the spirit of Gromov, and relate these invariants toso-called ball volume processes. Under mild dependence conditions, a weak invariance principle in $D([0,1]\times [0,\mathscr{R}])$ is established for sequential empirical versions of the latter, assuming the probabilistic structure possibly changes over time. Finally, we use this result to introduce novel test statistics for topological change, which are distribution-free in the limit under the hypothesis of stationarity. We explore these test statistics on time series of single-cell mRNA expression data, using shape descriptors coming from topological data analysis.
Student Seminar: Charlotte Dai
5:30–6:00 pm Jones 111
Thursday, May 8, 2025, at 5:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis l Presentation
Charlotte Dai, Department of Statistics, The University of Chicago
“Tracing Public Sentiment on Assisted Dying in U.S. Discourse (1930–2010): A Long-Form Text Analysis Using NLP”
Student Seminars: Matthew Kang
5:00–5:30 pm Jones 111
Wednesday, May 7, 2025, at 5:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis l Presentation
Matthew Kang, Department of Statistics, The University of Chicago
“Forecasting Gamma Squeezes in GameStop Stock”
Billingsley Lectures on Probability: Nina Holden
4:00–5:00 pm Jones 303
We are pleased to have Prof. Nina Holden, Courant Institute of Mathematical Sciences, New York University, as our honored speaker on Thursday, May 8, 2025, at 4:00 PM, Jones 303, 5747 South Ellis Avenue. Proceeding the Lecture, a tea will be held in Jones 304 at 3:30. A reception will immediately follow the lecture at 5:20 pm, in the Stevanovich Center for Financial Mathematics, 5727 South University Avenue.
Title: Scaling limits of random planar maps
Abstract: Planar maps are graphs embedded in the sphere such that no two edges cross, where we view two planar maps as equivalent if we can get one from the other via a continuous deformation of the sphere. In this talk we will present scaling limit results (i.e., convergence results) for random planar maps and we will focus in particular on a notion of convergence known as convergence under conformal embedding.