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.