2025
Student Seminar: Raphael Rossellini
11:00 am–12:30 pm DSI 322
Monday, December 15, 2025, at 11:00 AM, in DSI 322, 5460 S. University Ave
Dissertation Proposal Presentation
Raphael Rossellini, Department of Statistics, The University of Chicago
“Testing and ensuring calibration for decision-makers”
Student Seminar: Or Goldreich
10:00–11:30 am Jones 111
Monday, December 15, 2025, at 10:00 AM, in Jones 111, 5747 S. Ellis Avenue
Dissertation Proposal Presentation
Or Goldreich, Department of Statistics, The University of Chicago
“TBA”
Student Seminar: Jimmy Lederman
2:00–3:30 pm Jones 111
Wednesday, December 10, 2025, at 2:00 PM, in Jones 111, 5747 S. Ellis Avenue
Dissertation Proposal Presentation
Jimmy Lederman, Department of Statistics, The University of Chicago
“Count-Based Data Augmentation for Flexible Probabilistic Modeling of Nonstandard Data”
Student Seminar: Benedetta Bruni
9:00 am–10:30 pm DSI Building, Room 322
Wednesday, December 10, 2025, at 9:00 AM, in Room 322, 5460 S University Ave
Dissertation Proposal Presentation
Benedetta Bruni, Department of Statistics, The University of Chicago
“A Generalized Bayesian Approach to Tree Models for Densities”
Student Seminar: Jeonghwan Lee
2:30–4:00 pm Jones 304
Tuesday, December 9, 2025, at 2:30 PM, in Jones 304, 5747 S. Ellis Avenue
Dissertation Proposal Presentation
Jeonghwan Lee, Department of Statistics, The University of Chicago
“Topics in modern statistical learning: Distribution shift and learning with synthetic data”
Student Seminar: Qi Chen
10:30–11:00 am Jones 111
Tuesday, December 9, 2025, at 10:30 AM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis l Presentation
Qi Chen, Department of Statistics, The University of Chicago
“Graphic model Geometry-Aware Hamiltonian Variational Auto-Encoder”
Student Seminar: Qiaosen Wang
9:00–10:30 am Jones 111
Tuesday, December 9, 2025, at 9:00 AM, in Jones 111, 5747 S. Ellis Avenue
Dissertation Proposal Presentation
Qiaosen Wang, Department of Statistics, The University of Chicago
“Beyond Classical Assumptions: Hardness and Hope in Adaptive Statistical Inference”
Student Seminar: Joonhyung Shin
2:00–3:30 pm DSI Building, Room 301
Monday, December 1, 2025, at 2:00 PM, in Room 301, 5460 S. University Ave
Dissertation Proposal Presentation
Joonhyung Shin, Department of Statistics, The University of Chicago
“Statistical problems with computational hardness: a statistical physics approach”
Statistics Colloquium: Matteo Sesia
11:30 am–12:30 pm Jones 303
Matteo Sesia, Department of Data Sciences and Operations, University of Southern California
Title: “Conformal Inference for Open-Set and Imbalanced Classification”
Abstract: This talk presents a conformal prediction method for classification in highly imbalanced and open-set settings, where there are many possible classes and not all may be represented in the data. Existing approaches require a finite, known label space and typically involve random sample splitting, which works well when there is a sufficient number of observations from each class. Consequently, they have two limitations: (i) they fail to provide adequate coverage when encountering new labels at test time, and (ii) they may become overly conservative when predicting previously seen labels. To obtain valid prediction sets in the presence of unseen labels, we compute and integrate into our predictions a new family of conformal p-values that can test whether a new data point belongs to a previously unseen class. We study these p-values theoretically, establishing their optimality, and uncover an intriguing connection with the classical Good–Turing estimator for the probability of observing a new species. To make more efficient use of imbalanced data, we also develop a selective sample splitting algorithm that partitions training and calibration data based on label frequency, leading to more informative predictions. Despite breaking exchangeability, this allows maintaining finite-sample guarantees through suitable re-weighting. With both simulated and real data, we demonstrate our method leads to prediction sets with valid coverage even in challenging open-set scenarios with infinite numbers of possible labels, and produces more informative predictions under extreme class imbalance.
Student Seminars: Ziyang Wei
11:00 am–12:30 pm Jones 304
Tuesday, November 18, 2025, at 11:00 AM, in Jones 304, 5747 S. Ellis Avenue
Dissertation Proposal Presentation
Ziyang Wei, Department of Statistics, The University of Chicago
“Asymptotic and finite-sample theory for stochastic gradient descent”