Abstract: Recent advances (e.g. in artificial intelligence or laboratory technology) promise to improve the process for generating scientific hypotheses. Understanding whether the promised advances have…
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Abstract: Given a family of pretrained models and a hold-out set, how can we construct a valid conformal prediction set while selecting a model that…
Abstract: Stochastic compartmental models are fundamental tools in epidemic modeling. A recent idea termed Dynamical Survival Analysis (DSA) replaces a stochastic population-level hazard in these…
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…