Biography

Dr. Hsin-Hsiung Bill Huang serves as an Associate Professor within the Department of Statistics and Data Science at the University of Central Florida (UCF). He attained his Ph.D. in Statistics from the University of Illinois at Chicago, complemented by two MS degrees from the Georgia Institute of Technology and National Taiwan University, alongside BA degrees in Economics and BS in Mathematics from National Taiwan University. Dr. Huang’s academic pursuits span Bayesian ultrahigh dimensional variable selection, regularized low-rank matrix-variate regression, clustering, classification, and dimension reduction.

His research endeavors are focused on tackling the complexities of big data analysis, engaging in interdisciplinary studies, and pioneering novel statistical methodologies for real-world data conundrums. He has pioneered innovative statistical approaches for spatial and temporal modeling, medical image reconstruction, and algorithms pertinent to threat detection and large-scale data modeling challenges. Recognized for his contributions, Dr. Huang received the UCF Research Incentive Award (RIA) in 2021, as well as funding from the UCF College of Sciences SEED program in 2023-24. His research is generously supported by grants such as the Algorithms of Threat Detection (ATD 2019: DMS-1924792 and ATD 2023: DMS-2318925) from the National Science Foundation (NSF), where he serves as a principal investigator (PI), and a co-investigator on an NIH grant. Dr. Huang’s team at UCF has achieved top placements in consecutive editions of the ATD Challenge competitions from 2021 to 2023. In recognition of his exceptional instructional contributions, he was honored with the Outstanding Instruction Award in the College of Sciences in 2024.

2023 ATD Award     2022 ATD Award     2021 ATD Award

Research

Bayesian modeling and computation, robust dimension re­duction, manifold learning, ultrahigh-dimensional variable selection, Bayesian cluster Process, algorithms for threat detection, medical image reconstruction, missingness imputation.

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