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DTSTART;TZID=America/Los_Angeles:20260901T090000
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DTSTAMP:20260812T161813Z
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SUMMARY:Shen\, J. (STAT) - Bayesian Modeling and Uncertainty Quantification for Verbal Autopsy Data
DESCRIPTION:Verbal autopsy (VA) is a well developed tool to collect information describing deaths outside of hospitals by conducting surveys to the relatives and caregivers of the deceased person. It is routinely-implemented in low and middle income countries\, where it often lacks sufficient resources to conduct the autopsy. The main task is to estimate both individual level cause-of-death probabilities and population level cause-specific mortality fractions. In this document\, we present three projects dealing with challenges current VA modeling faces. In the first project\, we build a shared latent class model for verbal autopsy\, which allows causes to share common symptom patterns. A truncated Bayesian nonparametric prior allows the number of latent classes to adapt to data\, while retaining a computationally tractable representation. We provide a general framework for few-shot learning of the VA data\, where limited labels can be combined in analysis with a potentially much larger collection of unlabeled symptom profile. Two complementary factorizations are considered to account for different types of distribution shift between source and target. In the second project\, we develop a conformal prediction procedure for verbal autposy. For each death\, we generate a conformal prediction set from existing VA model outputs\, which guarantees a marginal coverage of true cause from a fequentist perspective. We also investigate conformal Bayesian procedures that more directly incorporate posterior uncertainty from Bayesian VA models. In the third project\, we consider situations where source and target do not share a common cause list. We propose methods using repulsive priors to identify deaths in the target domain whose symptom profiles are not adequately represented by the known causes in the source domain. \nEvent Host: Jibo Shen\, Ph.D. Student\, Statistical Science \nAdvisor: Zehang Richard Li \nZoom: https://ucsc.zoom.us/j/94158273558?pwd=VZORHL8P5O9bfb5JpSMZAL1DOM44uC.1&jst=2 \nPasscode:  294401
URL:https://live-events-ucsc.pantheonsite.io/event/shen-j-stat-bayesian-modeling-and-uncertainty-quantification-for-verbal-autopsy-data/
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260909T100000
DTEND;TZID=America/Los_Angeles:20260909T120000
DTSTAMP:20260819T162005Z
CREATED:20260819T162005Z
LAST-MODIFIED:20260819T162005Z
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SUMMARY:Chen\, Y. (STAT) - Flexible Bayesian Models for High-Dimensional and Longitudinal Discrete Data in Microbiome Studies
DESCRIPTION:Multivariate dependent discrete data routinely arise in microbiome studies. Analyzing these data presents interesting statistical challenges\, such as high dimensionality\, excess zeros\, large heterogeneity across samples\, and temporal dependence in longitudinal studies. Drawing inferences about objects of primary scientific interest—such as temporal trajectories of microbial abundance\, microbial interactions\, clusters of microbes similarly associated with environmental factors\, or networks of conditional dependencies—is more challenging due to these complexities\, requiring careful statistical modeling. Motivated by longitudinal microbiome experiments and large-scale fungal community surveys\, we propose flexible Bayesian models in which these objects are represented through low-dimensional or sparse structures\, regularized by global–local shrinkage priors\, and reported with uncertainty propagated through posterior inference. We first develop a Bayesian dynamic latent factor model for multivariate longitudinal count data. A rounded multivariate log-normal kernel links the observed counts to latent Gaussian variables\, and treatment-specific temporal trends of microbial abundance are modeled through Bayesian penalized B-splines. To better capture temporal changes in the mean\, dependence among microbial features is modeled through a low-dimensional factor structure with a Dirichlet–horseshoe+ shrinkage prior on the loadings. In addition\, continuous-time Ornstein–Uhlenbeck processes are used for the latent factors to account for temporal dependence within a subject. We next develop a sparse Bayesian probit regression model for high-dimensional presence–absence data to infer clusters of microbes whose presence has similar associations with environmental covariates. Due to the large number of microbes and excess zeros\, we first estimate a high-dimensional regression coefficient matrix using sparsity-inducing priors\, and then cluster the microbes based on the coefficient estimates. By using the posterior distribution of the coefficients\, we provide point estimates along with uncertainty quantification. Lastly\, we develop a Bayesian model that infers a time-varying precision matrix for longitudinal microbiome count data. While a covariance matrix describes marginal dependence\, a precision matrix characterizes conditional dependence among microbial features\, defining a microbial association network. By allowing this precision structure to evolve over time\, we obtain inferences about dynamic microbial networks. \nEvent Host: Yongqi Chen\, Ph.D. Student\, Statistical Science \nAdvisor: Juhee Lee \nZoom: https://ucsc.zoom.us/j/99307567941?pwd=NRnSLblnMKXEgqDRXPdBIFaRS8ctsq.1 \nPasscode: 301374
URL:https://live-events-ucsc.pantheonsite.io/event/chen-y-stat-flexible-bayesian-models-for-high-dimensional-and-longitudinal-discrete-data-in-microbiome-studies/
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20261007T120000
DTEND;TZID=America/Los_Angeles:20261007T130000
DTSTAMP:20260821T230735Z
CREATED:20260821T230735Z
LAST-MODIFIED:20260821T230735Z
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SUMMARY:Engineering Teaching Community (Faculty)
DESCRIPTION:During the chaos of a quarter\, is it hard to find time to reflect and improve as an instructor? Would you like to be a part of an inclusive\, supportive group of engineering instructors who do this in community? ETC is for sharing teaching experiences\, classroom ideas\, research on learning\, and methods that support instructors and students. All are welcome\, and lunch is provided. Please reach out to Jenny Quynn with questions.
URL:https://live-events-ucsc.pantheonsite.io/event/engineering-teaching-community-faculty-3/2026-10-07/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Meetings & Conferences,Training
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