Abstract: We introduce a novel framework for scalable and flexible variational inference targeting the non-Gaussian posterior of a latent continuous function or field. For both…
Abstract: As the world warms, we are experiencing an expected increase in summertime heat extremes. However, due to the large magnitude of internal variability in…
Abstract: We analyze weekly U.S. prescription claims data over a 36-month period spanning the COVID-19 pandemic to investigate patterns of overconsumption of the antiparasitic drug…
Abstract: In survival analysis, estimating the conditional survival function given predictors is often of interest. There is a growing trend in the development of deep…
Abstract: In this talk, I will present recent advances in developing manifold learning algorithms for biomedical data analysis that explicitly account for underlying structural information.…
Abstract: Teaching recommendations for implementing data science at the introductory level include placing greater emphasis on predictive modelling. In contrast to how simple linear regression…
Abstract: We consider linear regression with covariates that are separable random elements in a general Hilbert space. We first develop a principal component analysis for…
Protein sequence data offer a massive natural experiment: across evolution, residue positions co-vary in ways that encode 3D structure, illuminate key evolutionary constraints, and reveal…
Abstract: We propose a tensor-based linear algebraic framework for density estimation and sampling. The method consists of two simple steps: first, smoothing the empirical density…
Abstract: Identifying covariates that modify treatment effects is a critical problem in causal inference. Yet existing data-adaptive methods lack rigorous error control, risking spurious findings…
Abstract: Modern decision-making systems, from online marketplaces to large language models (LLMs), increasingly rely on high-dimensional human feedback, where heterogeneous user preferences and massive feature…
Spring 2026 Commencement Schedule Sunday, June 14 8:30 a.m. - 10:30 a.m. Bren Events Center View the commencement website for more information.