2025
Student Seminar: Wanyi Ling
2:00–2:30 pm Jones 111
Tuesday, April 15, 2025, at 2:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis l Presentation
Wanyi Ling, Department of Statistics, The University of Chicago
“An empirical partially Bayes method for adjusting batch effects”
Statistics Colloquium: Ashwin Pananjady
11:30 am–12:30 pm Jones 303
Ashwin Pananjady, H. Milton Stewart School of Industrial and Systems Engineering/The School of Electrical and Computer Engineering, Georgia Institute of Technology
Title: Predicting the behavior of complex iterative algorithms with random data
Abstract: Iterative algorithms are the workhorses of modern statistical signal processing and machine learning. Algorithm design and analysis is largely based on variational properties of the optimization problem, and the classical focus has been on obtaining convergence guarantees over classes of problems that possess certain types of geometry. However, modern optimization problems in statistical settings are high-dimensional and involve random data, and algorithms often behave differently from what is suggested by classical theory. With the motivation of better understanding optimization in such settings, I will present a toolbox for deriving “state evolutions” for a wide variety of algorithms with random data. These are non-asymptotic, near-exact predictions of the statistical behavior of the algorithm, which apply even when the underlying optimization problem is nonconvex or the algorithm is randomly initialized. We will showcase these predictions on deterministic and stochastic variants of complex algorithms employed in some canonical statistical models.
Student Seminar: Sean Richardson
12:30–1:00 pm Jones 111
Thursday, April 10, 2025, at 12:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis l Presentation
Sean Richardson, Department of Statistics, The University of Chicago
“Causal Evaluation of Black-Box Reward Models via Textual Interventions”
Student Seminars: Yuepeng Yang
11:00 am–1:00 pm Jones 304
Thursday, April 10, 2025, at 11:00 AM, in Jones 304, 5747 S. Ellis Avenue
PhD Dissertation Defense Presentation
Yuepeng Yang, Department of Statistics, The University of Chicago
“Statistical estimation with heterogeneous data structures”
Student Seminar: Omar Al-Ghattas
9:00–11:00 am Jones 304
Thursday, April 10, 2025, at 9:00 AM, in Jones 304, 5747 S. Ellis Avenue
PhD Dissertation Defense Presentation
Omar Al-Ghattas, Department of Statistics, The University of Chicago
“Non-asymptotic statistical analysis of ensemble based filtering algorithms”
John Reinitz - Memorial Service
3:00–4:30 pm Bond Chapel
A memorial to honor Dr. John Reinitz will be held in Bond Chapel with a reception at the Quad Club afterwards.
Student Seminar: Yu Gui
11:30 am–1:30 pm Jones 304
Tuesday, April 8 2025, at 1:00 PM, in Jones 304, 5747 S. Ellis Avenue
PhD Dissertation Defense Presentation
Yu Gui, Department of Statistics, The University of Chicago
“Statistical Learning and Inference in Weakly Specified Settings: Shifted Distributions and Unlabeled Data”
Student Seminars: Rohan Hore
1:00–3:00 pm Searle 236
Monday, April 7, 2025, at 1:00 PM, in Searle 236, 5735 S. Ellis Avenue
PhD Dissertation Defense Presentation
Rohan Hore, Department of Statistics, The University of Chicago
“Assumption-lean approaches to modern statistical inference”
Statistics Colloquium: David Dunson
11:30 am–12:30 pm Jones 303
David Dunson, Statistical Science and Department of Mathematics, Duke University
Title: Deep latent class regression
Abstract: High-dimensional categorical data arise in diverse scientific domains and are often accompanied by covariates. Latent class regression models are routinely used in such settings, reducing dimensionality by assuming conditional independence of the categorical variables given a single latent class that depends on covariates through a logistic regression model. However, such methods become unreliable as the dimensionality increases. To address this, we propose a flexible family of deep latent class models. Our model satisfies key theoretical properties, including identifiability and posterior consistency, and we establish a Bayes oracle clustering property that ensures robustness against the curse of dimensionality. We develop efficient posterior computation methods, validate them through simulation studies, and apply our model to joint species distribution modeling in ecology. The theory and methods can be easily extended beyond categorical observed data.
Joint work with Yuren Zhou & Yuqi Gu
Student Seminars: Subhodh Kotekal
3:30–5:30 pm Jones 304
Friday, April 4, 2025, at 3:30 PM, in Jones 304, 5747 S. Ellis Avenue
PhD Dissertation Defense Presentation
Subhodh Kotekal, Department of Statistics, The University of Chicago
“Minimax hypothesis testing in large-scale inference”