2026
Billingsley Lectures on Probability: Christophe Garban
5:00–6:00 pm Kent 120
Billingsley Lectures on Probability
Reception immediately following the lecture at 6:10 pm, in Jones 111, 5747 S Ellis Ave.
Christophe Garban
Université Lyon 1/Courant - NYU
Title: Continuous Symmetry and Phase Transitions in Lattice Spin Systems
Abstract: A central problem in statistical physics is to understand how spins placed on the lattice Z^d interact and collectively organize at different temperatures. When the spins take values in a discrete set — for instance in the celebrated Ising model, where \sigma_x\in\{−1,+1\} — the mechanisms governing phase transitions are by now relatively well understood.
The situation changes dramatically when the spins take values in a continuous space, such as the unit circle S^1 in the XY model or the unit sphere S^2 in the classical Heisenberg model. In this setting, new phenomena appear, and the behavior depends strongly on whether the underlying symmetry is Abelian or non-Abelian. In particular, the non-Abelian case remains far more mysterious.
In this talk, I will introduce the mathematics of spin systems with continuous symmetry, emphasizing their deep connections with analysis, including harmonic functions, harmonic maps, and geometric analysis. I will also describe some recent results and open problems in the area.
No prior background in statistical physics or probability will be assumed. Based on joint works with J. Aru, D. van Engelenburg, P. Dario, N. de Montgolfier, A. Sepúlveda and T. Spencer.
Reception immediately following the lecture at 6:10 pm, in Jones 111, 5747 S Ellis Ave.
Joint Statistics and DSI Colloquium: Jiaqi Zhang
4:00–5:00 pm DSI 105
Jiaqi Zhang
PhD Candidate
Massachusetts Institute of Technology
Title: Modeling Large-Scale Interventions
Abstract: Complex causal mechanisms among genes govern cellular functions in health and disease. Understanding these mechanisms can accelerate therapeutic discovery but remains challenging due to the large number of genes and their intricate dependencies. Recent advances in experimental technologies are making this problem increasingly tractable: it is now possible to systematically intervene on individual genes or gene combinations in single cells and measure their downstream effects, enabling empirical identification and validation of causal relationships. However, interventional data are high-dimensional, making interpretation challenging, and costly to collect.
In this talk, I will present our work tackling these challenges from three aspects. First, we introduced causal representation theories and algorithms with identifiability guarantees to uncover latent variables behind high-dimensional data. Second, we developed a method to model interventional data that can predict the effects of novel interventions with high accuracy, incorporating both distributional shifts and prior domain knowledge. Finally, we showed how predictive intervention modeling can improve future experimental design, illustrated by an application where we predicted and validated previously unknown T-cell regulators with therapeutic potential for cancer immunotherapy.
Student Seminar: Brian Ping_Huan Wu
3:30–4:00 pm Jones 304
Tuesday, February 24, 2026, at 3:30 PM, in Jones 304, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Brian Ping-Huan Wu, Department of Statistics, The University of Chicago
“Fast Estimation and Valid Statistical Inference for Mixed-Effect Location-Scale Models Using Variational Inference”
Joint Statistics and DSI Colloquium: Mateo Díaz
11:30 am–12:30 pm DSI 105
Mateo Díaz
Assistant Professor
Department of Applied Mathematics and Statistics
Mathematical Institute for Data Science
Johns Hopkins University
Title: Leveraging Structure for Faster Algorithms in Optimization and Diffusion
Abstract: Large-scale iterative methods drive modern AI, yet their theoretical foundations often lag behind their empirical success. We argue that bridging this gap requires identifying the inherent problem structure that enables these algorithms to perform well. This talk instantiates this principle across two domains: optimization and generative modeling.
First, we derive new theoretical guarantees for the Levenberg–Morrison-Marquardt method. Although this method is ubiquitous in settings that demand highly accurate solutions—for instance, when training physics-informed neural networks for scientific discovery—classical guarantees do not explain its strong empirical performance in modern overparameterized, ill-conditioned regimes. By reframing it through the lens of composite optimization, we uncover geometric conditions that ensure fast convergence even in these challenging modern regimes.
Second, we introduce Proximal Diffusion Models (PDM). While standard diffusion models rely on score-matching and forward discretization, we demonstrate that a backward discretization using proximal maps offers significant theoretical and practical advantages. Under mild conditions, we prove that PDM achieves $\varepsilon$-accuracy in KL-divergence within $\widetilde{O}(d/\sqrt{\varepsilon})$ steps and empirically demonstrate that it outperforms conventional methods using fewer sampling iterations.
DSI Distinguished Speaker Series: Jeffrey Heer
12:30–2:30 pm DSI 105
Jeffrey Heer
Jerre D. Noe Endowed Professor of Computer Science & Engineering
University of Washington
Title: Augmenting Data Scientists: The Promise and Peril of AI-Assisted Analysis
Abstract: Abstract: Data analysis is a rich sensemaking process, with frequent shifts among data representations, tools, and both conceptual & mathematical models. Computational methods can go beyond fitting models and rendering charts to make in-context recommendations and even guide end-to-end analysis workflows. How does the design of such tools affect people’s exploration, modeling, and understanding of data? In this talk, we will consider methods for augmenting data science work by integrating proactive computational support into interactive tools, with the goal of providing algorithmic assistance to augment and enrich, rather than replace, people’s intellectual work. Across tasks such as data transformation, visualization, and statistical modeling, we apply artificial intelligence to bridge gaps between user intent and robust analysis results. At the same time, we need to pay careful attention to ways these methods may exacerbate bias, foster dependence, and pose challenges for the future of data analysis.
Student Seminar: Boxuan Zhang
3:00–3:30 pm Jones 111
Thursday, February 19, 2026, at 3:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Boxuan Zhang, Department of Statistics, The University of Chicago
“Conformal Prediction for Bayesian Posterior”
Joint Computer Science and Data Science Institute Seminar: Shreya Shankar
2:30–3:30 pm DSI 105
Shreya Shankar
PhD Candidate in the Data Systems and Foundations Group
University of California, Berkeley
Title: Building Effective Unstructured Data Systems
Abstract: Databases and other data systems have successfully democratized data-oriented computation across domains, thanks to decades of research in system internals and end-user interfaces. However, such systems center on structured (i.e., tabular) data; unstructured data—the vast majority of data—has largely been ignored. Large language models (LLMs) now give us a building block for unstructured data analysis, and we face the same questions as in the early days of data systems—e.g., how should users author queries? How do we efficiently execute queries at scale?—but many well-established tenets from traditional data systems no longer hold. In my talk, I will present DocETL, a system I developed for unstructured data analysis. I will discuss how we had to rethink query optimization under these new assumptions, optimizing user-written pipelines for both accuracy and efficiency—as well as end-user interfaces for authoring, iterating on, and debugging pipelines. DocETL is open-source with 3.5k+ GitHub stars; our hosted interface has supported 4.1k+ pipelines across 30+ S&P-500 industries. Query optimization ideas from our work have been adopted in databases such as Snowflake and BigQuery, and our interface design principles have been adopted by companies like LangChain and OpenAI.
Student Seminar: Yushuo Li
2:00–2:30 pm Jones 111
Wednesday, February 18, 2026, at 2:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Yushuo Li, Department of Statistics, The University of Chicago
“Asymptotically Optimal Conformal Prediction for Classification”
Student Seminar: Buning (Erica) Fan
1:30–2:00 pm Jones 111
Wednesday, February 18, 2026, at 1:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Buning Fan, Department of Statistics, The University of Chicago
“Comparing Bayesian Software Platforms for Three-Level Mixed Effects Location Scale Models”
Student Seminar: Zixuan Qin
1:00–1:30 pm Jones 111
Wednesday, February 18, 2026, at 1:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Zixuan Qin Department of Statistics, The University of Chicago
“Operator Learning and Bispectrum-Guided Diffusion for Functional Multi-Reference Alignment”