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DTSTART;TZID=America/Los_Angeles:20260818T010000
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SUMMARY:ReView
DESCRIPTION:Mary Porter Sesnon Art Gallery\nAugust 13-September 19\, 2026 \nOpening celebration and Artist Talk\nSaturday\, August 15\, 2-4:30 p.m\nLight refreshments will be served. \nSummer Gallery Hours: Thursday-Saturday\, 1-5pm \nReView: UC Santa Cruz Arts Alumni Exhibition features Arts Division alumni from the Monterey and San Francisco Bay Areas. Curated by Valéria Miranda\, the Sesnon’s Director\, ReView includes a wide variety of media and techniques highlighting the diversity of contemporary practice of alumni who graduated from 1972 to 2025. The exhibition title alludes to the portfolio critique process and suggests ideas of reflection and reevaluation—whether revisiting past work\, rethinking materials\, or tracing how a practice evolves over time. \nThis exhibition celebrates the creative path of artists who began their journeys at UC Santa Cruz. United by shared roots yet defined by distinct paths\, the participating alumni reflect the lasting influence of the Arts Division programs. Their works speak to the spirit of inquiry\, experimentation\, and perseverance that has long shaped the university’s artistic community. \nReView presents an opportunity for the campus and larger community to see the professional practice of these artists who continue to build on the creative foundation that they received at UCSC and to inspire our current students\, offering visualization of their own future artistic journeys after graduation. \nReView offers a unique opportunity for returning alumni to celebrate their creative expressions while reconnecting with classmates and the university community. \nAdmission is free and open to the public.  \nMore Information
URL:https://events.ucsc.edu/event/review/2026-08-18/
LOCATION:Mary Porter Sesnon Art Gallery\, Baskin Service Road\, Santa Cruz\, CA\, 95064
CATEGORIES:Exhibits
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DTSTART;TZID=America/Los_Angeles:20260901T090000
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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://events.ucsc.edu/event/shen-j-stat-bayesian-modeling-and-uncertainty-quantification-for-verbal-autopsy-data/
CATEGORIES:Ph.D. Presentations
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