2026
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.
Student Seminar: Syris Shao
10:30–11:00 am Jones 111
Monday, April 13, 2026, at 10:30 AM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Syris Shao, Department of Statistics, The University of Chicago
“Comparing Univariate and Multivariate Models to Forecast Portfolio Value-at-Risk During COVID-19”
Student Seminar: Linzhe Teng
1:30–2:00 pm Jones 111
Friday, April 10, 2026, at 1:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Linzhe Teng, Department of Statistics, The University of Chicago
“GLIMES-Based Differential Expression with Comparative Low-Dimensional Representations in Paired scRNA-seq of BBIBP-CorV Vaccination”
DSI Distinguished Speaker Series: Lillian Lee
12:00–1:30 pm DSI 105
Lillian Lee
Charles Roy Davis Professor of Computer Science
Cornell University
Title: Taking a turn for the better? Pivoting and pivotal moments in consequential conversations
Abstract: So much of human interaction occurs as conversations, and it is both fascinating and imperative to analyze them. Recently, my co-authors and I have turned to texting-based conversations between mental-health therapists or crisis counselors and their clients, seeking to identify “key” moments in these exchanges:
(1) A “pivoting” moment corresponds to a *redirection* of the conversation introduced by one party that is accepted/followed by the other. We develop a probabilistic measure of how much an utterance immediately redirects the flow of the conversation, accounting for both the intention and the actual realization of such a change.
(2) In a *pivotal* moment, the conversation’s outcome hangs in the balance: how one responds can put the conversation on substantially diverging trajectories leading to significantly different results. We formalize this intuition by estimating the variance in expectation of outcome depending on what might be said next.
We find significant correlates of our measures in real human conversations on widely-used platforms. For example, the patients in our longer-term mental-health-therapy data who redirected less in their first few sessions were significantly more likely to eventually express dissatisfaction with their therapist and terminate the relationship; and the staff responses in our crisis-counseling data had greater estimated impact on disengagement rates during pivotal moments than in non-.
Joint work with Vivian Nguyen, Cristian Danescu-Niculescu-Mizil, Thomas D. Hull, and Sang Min (Dave) Jung.
Student Seminar: Claire Tseng
10:00–11:00 am Jones 111
Wednesday, April 8, 2026, at 10:00 AM, in Jones 111, 5747 S. Ellis Avenue
Dissertation Proposal Presentation
Claire Tseng, Department of Statistics, The University of Chicago
“Bias and Implied Beliefs in Large Language Models for Economic Expectations”
Statistics Colloquium: Marco Avella Medina
11:30 am–12:30 pm Jones 303
Marco Avella Medina
Department of Statistics
Columbia University
Title: A Theoretical Framework for M-Posteriors: Frequentist Guarantees and Robustness Properties
Abstract: We provide a theoretical framework for a wide class of generalized posteriors that can be viewed as the natural Bayesian posterior counterpart of the class of M-estimators in the frequentist world. We call the members of this class M-posteriors and show that they are asymptotically normally distributed under mild conditions on the M-estimation loss and the prior. In particular, an M-posterior contracts in probability around a normal distribution centered at an M-estimator, showing frequentist consistency and suggesting some degree of robustness depending on the reference M-estimator. We formalize the robustness properties of the M-posteriors by a new characterization of the posterior influence function and a novel definition of breakdown point adapted for posterior distributions. We illustrate the wide applicability of our theory in various popular models and discuss extensions to variational inference. We illustrate their empirical relevance of our results in some numerical examples.
This is based on joint work with Juraj Marusic and Cynthia Rush
Statistics Colloquium: Cynthia Rudin
11:30 am–12:30 pm Jones 303
Cynthia Rudin
Department of Computer Science
Duke University
Title: Many Good Models Leads To…
Abstract: As it turns out, many good models leads to amazing things! The Rashomon Effect, coined by Leo Breiman, describes the phenomenon that there exist many equally good predictive models for the same dataset. This phenomenon happens for many real datasets and when it does, it sparks both magic and consternation, but mostly magic. In light of the Rashomon Effect, my collaborators and I propose to reshape the way we think about machine learning, particularly for tabular data problems in the nondeterministic (noisy) setting. I’ll address how the Rashomon Effect impacts (1) the existence of simple-yet-accurate models, (2) flexibility to address user preferences, such as fairness and monotonicity, without losing performance, (3) algorithm choice, specifically, providing advanced knowledge of which algorithms might be suitable for a given problem, (4) public policy, and (5) scientific discovery. I’ll also discuss a theory of when the Rashomon Effect occurs and why: interestingly, noise in data leads to a large Rashomon Effect. My goal is to illustrate how the Rashomon Effect can have a massive impact on the use of machine learning for complex problems in society.
I’ll be mainly discussing the paper “Amazing Things Come From Having Many Good Models” (ICML spotlight, 2024) which is joint work with Chudi Zhong, Lesia Semenova, Margo Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, and Zachery Boner.
Joint Statistics and DSI Colloquium: Soledad Villar
2:00–3:00 pm DSI 105
Soledad Villar
Assistant Professor
Johns Hopkins University
Title: Machine Learning and Symmetries
Abstract: Symmetries play a significant role in machine learning. In scientific applications, they often arise as constraints imposed by physical laws. More broadly, symmetries emerge whenever objects admit multiple ways to express them (for example, in graph machine learning). In addition, modern machine learning models are heavily overparameterized, so many distinct sets of parameters can represent the same function, revealing further underlying symmetries.
In this talk, we describe methods for incorporating symmetries into machine learning models using classical tools from algebra, including invariant theory and Galois theory. A particularly interesting feature of symmetry-preserving models is that they can be defined independently of the size or dimension of the input. The formalization of this setting, known as any-dimensional machine learning, is inspired by ideas from representation stability. In this talk we present a theoretical framework for understanding the assumptions imposed by such models, which allows us to align learning models with data of varying sizes and learning tasks in a principled way.
Any-dimensional models use a fixed set of parameters and can be evaluated on data of varying sizes. Hyperparameter transfer considers the complementary setting, in which the data are fixed while the model size varies, and studies how optimal hyperparameters (such as the learning rate) can be transferred from smaller models to larger ones. If time permits, we will also discuss recent connections between any-dimensional machine learning and hyperparameter transfer.
Student Seminar: Chengran Yang
2:00–2:30 pm Jones 111
Thursday, March 5, 2026, at 2:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Chengran Yang Department of Statistics, The University of Chicago
“Can Machine Learning learn Weak Signal? ——Extending to Binary Logistic Models”
Student Seminar: Sili (Shelly) Wang
9:00–9:30 am Jones 111
Wednesday, March 4, 2026, at 9:00 AM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Sili (Shelly) Wang, Department of Statistics, The University of Chicago
“TBA”