Seminar 2023-10-13

From Algorithms for Anomaly Detection to Spatial and Temporal Modeling and Bayesian Ultra-High Dimensional Variable Selection

Hsin-Hsiung Huang

Associate Professor

Department of Statistics and Data Science

University of Central Florida

Date: Friday, October 13, 2023

Time: 11:00 A.M. – 12:00 P.M. Eastern Time

Location: Johnson Center, Room 325 Meeting Room A

Inspired by our investigation on spatiotemporal data analysis for the NSF ATD challenges, we've investigated Bayesian clustering, variable selection for mixed-type multivariate responses and Gaussian process priors for spatiotemporal data. The proposed Bayesian approaches effectively and efficiently fit high-dimensional data with spatial and temporal features. We further propose a two-stage Gibbs sampler which leads a consistent estimator with a much faster posterior contraction rate than a one-step Gibbs sampler.  For Bayesian ultrahigh dimensional variable selection, we have developed Bayesian sparse multivariate regression for mixed responses (BS-MRMR) with shrinkage priors model for mixed-type response generalized linear models. We consider a latent multivariate linear regression model associated with the observable mixed-type response vector through its link function. Under our proposed BS-MRMR model, multiple responses belonging to the exponential family are simultaneously modeled and mixed-type responses are allowed. We show that the MBSP-GLM model achieves posterior consistency and quantifies the posterior contraction rate. Additionally, we incorporate Gaussian processes into zero-inflated negative binomial regression. To conquer the computation bottleneck that GPs may suffer when the sample size is large, we adopt the nearest-neighbor GP approach that approximates the covariance matrix using local experts. We provide simulation studies and real-world gene data examples.

Dr. Hsin-Hsiung Bill Huang is an Associate Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF). Dr. Huang received his Ph.D. in Statistics from the University of Illinois at Chicago and two MS degrees from the Georgia Institute of Technology and National Taiwan University as well as the BA in Economics and BS in Mathematics degrees from National Taiwan University. His scholarly interests and expertise include Bayesian ultrahigh dimensional variable selection, regularized low-rank matrix-variate regression, clustering, classification, and dimension reduction.

His research addresses challenges in analyzing big data, interdisciplinary research, and developing new statistical methods for real-data challenges. He has developed new statistical methods for computed tomography (CT), developing a statistical reconstruction algorithm for positronium lifetime imaging using time-of-flight positron emission tomography (PET) and interdisciplinary research of developing algorithms for threat detection and large spatiotemporal data modeling challenges. He is awarded the UCF Research Incentive Award (RIA) in 2021. His current research is partially sponsored by his grant of the Algorithms of Threat Detection (ATD) of the National Science Foundation (NSF) as a principal investigator (PI) in 2019 with a supplement grant in 2023 and a new ATD grant in 2023 and an NIH R01 grant as a co-investigator in 2019. His team named UCF has won the top places in a row of the 2021 and 2022 ATD challenge competitions.

Nicholas Rios

David Kepplinger