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Statistics Colloquium: Ping-Shou Zhong
11:30 am–12:30 pm Jones 303
Ping-Shou Zhong
Professor
University of Illinois Chicago
Title: On the Adaptivity and Scalability of Kernel Methods for Testing and Prediction
Abstract: Kernel methods are widely used for prediction and hypothesis testing. In this talk, I will introduce two approaches that enhance the scalability and adaptivity of kernel methods. In the first part, we introduce a new family of adaptive, distribution-free tests of independence based on binary expansion coefficients. By characterizing independence through cross-covariances of multiscale interaction terms, the proposed method is applicable in a general setting and does not require the reproducing kernel Hilbert space assumption. The resulting tests admit an explicit kernel representation, enabling efficient computation while reducing sensitivity to kernel choice. In the second part, we propose an informative sub-data selection method for large-scale kernel learning. This method identifies a representative subset of observations, enabling model training on a substantially reduced yet informative sample. The approach provides a principled form of data reduction and integrates naturally with existing kernel approximation and sketching techniques.
Students Seminar: Yuguan Wang
10:00–11:00 am Jones 111
Wednesday, April 15, 2026, at 10:00 AM, in Jones 111, 5747 S. Ellis Ave.
Dissertation Defense Presentation
Yuguan Wang, Department of Statistics, The University of Chicago
“Fast Algorithms via Compressed Moment Representations”
Student Seminars: Xiaohan Zhu
11:00 am–12:30 pm DSI Room 103
Thursday, April 16, 2026, at 11:00 AM, in DSI Room 103, 5460 S University Ave.
Dissertation Defense Presentation
Xiaohan Zhu, Department of Statistics, The University of Chicago
“TBA”
Student Seminars: Jeonghwan Lee
2:30–4:00 pm Jones 111
Friday, April 17, 2026, at 2:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Jeonghwan Lee, Department of Statistics, The University of Chicago
“TBA”
Student Seminars: Jinwen Yang
4:30–6:00 pm Jones 111
Friday, April 17, 2026, at 4:30 PM, in Jones 111, 5747 S. Ellis Ave.
Dissertation Defense Presentation
Jinwen Yang, Department of Statistics, The University of Chicago
“TBA”
Statistics Colloquium: Yiqiao Zhong
11:30 am–12:30 pm Jones 303
Yiqiao Zhong
Department of Statistics
University of Wisconsin-Madison
Title: Compositionality in Large Language Models: Emergence, Generalization, and Geometry
Abstract: Large language models (LLMs) have demonstrated remarkable reasoning abilities through novel techniques such as in-context learning and chain-of-thought (CoT) reasoning. Empirically, key reasoning skills often emerge only at larger scales or after prolonged training. Yet the underlying mechanism of LLM reasoning—-how compositional representations are formed and organized—-remains poorly understood.
In this talk, I present recent progress toward uncovering emergent compositional structure through controlled synthetic experiments on small transformers and targeted intervention studies on modern LLMs. First, I show that learning a key compositional structure is essential for out-of-distribution generalization, and that this process undergoes sharp phase transitions during training. At a critical stage, an intermediate low-dimensional “bridge subspace” emerges, serving as a shared representation connecting multiple layers. Second, using arithmetic composition as a minimal testbed for CoT reasoning, I demonstrate that autoregressive training on reasoning traces exhibits distinct reasoning phases. In particular, causally faithful reasoning emerges only when training noise lies below a critical threshold.
Together, these findings suggest that core statistical principles such as low-dimensional subspaces and causality may provide key foundations for advancing the interpretability and transparency of LLMs.
Student Seminars: Soham Bonnerjee
3:00–4:00 pm Room 106
Monday, April 20, 2026, at 3:00 PM, in Room 106, 1118. E. 58th St.
Dissertation Defense Presentation
Soham Bonnerjee, Department of Statistics, The University of Chicago
“TBA”
Student Seminars: Sean O'Hagan
1:00–3:00 pm Jones 111
Wednesday, April 22, 2026, at 1:00 PM, in Jones 111, 5747 S. Ellis Ave.
Dissertation Defense Presentation
Sean O’Hagan, Department of Statistics, The University of Chicago
“TBA”
Students Seminar: Kiho Park
1:00–3:00 pm Room 103
Thursday, April 23, 2026, at 1:00 PM, in Room 103, 5460 S University Ave.
Dissertation Defense Presentation
Kiho Park, Department of Statistics, The University of Chicago
“TBA”
Statistics Colloquium: Stefan Wager
11:30 am–12:30 pm Jones 303
Stefan Wager
Department of Statistics
Stanford University
Title: TBA
Abstract: TBA
Bahadur Memorial Lectures: Nancy Reid (Day 1)
11:30 am–12:30 pm Jones 303
Title: “Lies, Damned Lies, and Statistics”
Abstract: This is the title I used the first time I taught the U of T First-Year Seminar course, many years ago. I was nervous about the prospect of giving a seminar-style course for students fresh from high school, and unsure how to distinguish it from a run-of-the-mill introductory statistics course. As it turned out, however, the experience had a big impact on my teaching, research, and views on statistical science. Although much has changed in our field in the years since, the basic principles of reasoning with uncertainty have not. In this talk I will reflect on my experiences in trying to convey the ongoing importance of statistical science and perhaps hazard a guess about the future.
Bahadur Memorial Lectures: Nancy Reid (Day 2)
3:00–4:00 pm DSI 105
Title: “All Models are Wrong”
Abstract: This talk will consider the assessment of semiparametric and other highly-parametrized models from the perspective of foundational principles of parametric statistical inference. It is cast as a generalised version of the Fisherian sufficiency/co-sufficiency separation, replacing out-of-sample prediction error by a type of within-sample prediction error. The theory is illustrated through several examples, including a post-reduction inference approach to confidence sets of sparse regression models. This is joint work with Heather Battey.
Statistics Colloquium: Aravindan Vijayaraghavan
11:30 am–12:30 pm Jones 303
Aravindan Vijayaraghavan
Department of Computer Science
Northwestern University
Title: TBA
Abstract: TBA
Statistics Colloquium: David Blei
11:30 am–12:30 pm Jones 303
David Blei
Departments of Statistics and Computer Science
Columbia University
Title: TBA
Abstract: TBA