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DTSTART;TZID=America/Los_Angeles:20260817T010000
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SUMMARY:Graduate Preparation Program
DESCRIPTION:The Graduate Preparation Program (GPP) is a four-week intensive non-credit course offered in person on the main campus prior to the fall quarter\, which is open to all current and newly admitted international graduate students. It focuses on English for Academic Purposes (EAP)\, academic skills\, and cultural orientation. The program also provides a foundation for transitioning into the Teaching Assistant role. \nThe course offers guided support with: \n\nSeminar-style classroom discussion\nPresentation and oral communication skills\nInformation on U.S. classroom culture; roles and responsibilities of faculty\, Teaching Assistants\, and students\nCritical thinking: using evidence to support ideas\nResearch writing\, citation\, and avoiding plagiarism\nCollaborative and project-based learning\n\nA collaboration between the Graduate Division and Global Engagement\, the Graduate Preparation Program provides a strong foundation for the U.S. classroom environment and university system\, in addition to the opportunity to practice and master the language and academic skills needed for success. \nFor more information and to register\, please visit our webpage.
URL:https://events.ucsc.edu/event/graduate-preparation-program/
CATEGORIES:Conference,Lectures & Presentations
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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://events.ucsc.edu/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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