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 AwardResearch
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.
Publications
1. Huang HH*, F Yu F and T Zhang* (2024) Robust Sufficient Dimension Reduction via -Distance Covariance. Journal of Nonparametric Statistics, 1(16) 1048-5252. DOI: 10.1080/10485252.2024.2313137
2. Zhang W, Ma Z, Ho HH*, Yang S, Habiger JD, Huang HH, Huang Y (2024) Multi-omics Integrative Analysis for Incomplete Data Using Weighted p- value Adjustment Approaches. Journal of Agricultural, Biological, and Environmental Statistics. https://doi.org/10.1007/s13253-024-00603-3
3. Chen Z, An L, Kao CM, Huang HH (2023) The properties of the positronium lifetime image reconstruction based on maximum likelihood estimation. Bio- Algorithms and Med-Systems 19 (1)
4. Huang HH, Yu F, Fan X, and Zhang T (2023) A Framework of Regularized Low-Rank Matrix Models for Regression and Classification. Statistics and Computing 34 (10), https://doi.org/10.1007/s11222-023-10318
5. He Q and Huang HH (2023) A Framework of Zero-Inflated Bayesian Negative Binomial Regression Models for Spatiotemporal Data. Journal of Statistical Planning and Inference. Volume 229, 106098, ISSN 0378-3758,
6. Huang HH and He Q (2023) Statistical modeling of Peromyscus maniculatus (deer mouse) amounts per trap with spatiotemporal data. Japanese Journal of Statistics and Data Science. https://doi.org/10.1007/s42081-023-00212-3
7. Chen CW and Huang HH (2023) Unsupervised Vessel Trajectory Reconstruction. Front. Appl. Math. Stat. 9, doi: 10.3389/fams.2023.1124091
8. He Q, Harrison CW, and Huang HH (2023) Detection of Anomalies in Traffic Flows with Large Amounts of Missing Data. The New England Journal of Statistics in Data Science, 1-11
9. Yu Z, Yang J, and Huang HH (2023) Smoothing Regression and Impact Measures for Accidents of Traffic Flows. Journal of Applied Statistics DOI: 10.1080/02664763.2023.2175799
10. Huang HH and He Q (2022) Nonlinear regression analysis. International Encyclopedia of Education 4th Edition
11. Harrison CW, He Q, and Huang HH (2022) Clustering Gene Expressions Using the Table Invitation Prior Genes 13 (11), 2036
12. Fan CW, Drumheller K, Chen IH, and Huang HH (2022) College students’ sleep difficulty during COVID-19 and correlated stressors: A large-scale cross-sessional survey study. Sleep Epidemiology 1, 100004
13. Huang HH and Yang J (2020) Affine-Transformation Invariant Clustering Models. Journal of Statistical Distributions and Applications 7 (1), 1-24
14. Huang HH and Zhang T (2020) Robust discriminant analysis using multi-directional projection pursuit. Pattern Recognition Letters, 138, 651-656.
15. Huang HH, Condor A, and Huang HJ. (2020) Classification of EEG Motion Artifact Signals Using Spatial ICA. Statistical Modeling in Biomedical Research: Contemporary Topics and Voices in the Field.
16. Zhu H, Huang HH, and Pang S. (2019) Photon allocation strategy in region-of-interest tomographic imaging. IEEE Transactions on Computational Imaging. DOI: 10.1109/TCI.2019.2922477
17. Huang HH, Wang Z, and Chung W. (2019) Efficient Parameter Selection for Support Vector Machines. Enterprise Information Systems. doi.org/10.1080/17517575.2019.1592233
18. Huang HH and Girimurugan SB. (2019) Discrete Wavelet packet transform based discriminant analysis for genome sequences data. Statistical Applications in Genetics and Molecular Biology. 20180045
19. Huang HH, Hao S, Alacorn S, and Yang J. (2018) Comparisons of classification methods for viral genomes and protein families using alignment-free vectorization. Statistical Applications in Genetics and Molecular Biology, 17(4), 20180004.
20. Huang HH and Girimurugan SB. (2018) A novel real-time genome comparison method using discrete wavelet transform. Journal of Computational Biology, 25(4), 406- 416
21. Huang HH, Wang Z, and Chung W. (2017) Efficient parameter selection for SVM: The case of business intelligence categorization, 2017 IEEE International Conference on Intelligence and Security Informatics (ISI) proceeding, 158-160.
22. Huang HH and Yu C. (2016) Clustering DNA sequences using the out-place measure with reduced n-gram. Journal of Theoretical Biology, 406, 61-72.
23. Huang HH. (2016) Ensemble method of k-mer and natural vector for the phylogenetic analysis of multiple-segmented viruses. Journal of Theoretical Biology, 398, 136-144.
24. Lu AT, Austin E, Bonner A, Huang HH, Cantor RM. (2014) Applications of machine learning and data mining methods to detect associations of rare and common variants with complex traits. Genet Epidemiol, 38 Suppl 1:S81-85.
25. Huang HH, Yu C, Hernandez T, Zheng H, Yau SC, He RL, Yang J, and Yau SST. (2014) Global Comparison of multiple-segmented Viruses in 12-dimensional Genome Space. Molecular Phylogenetics and Evolution, 81, 29-36.
26. Huang HH, Xu T and Yang J. (2014) Comparing logistic regression, support vector machines, and permanental classification methods in predicting hypertension. BMC Proceedings, 8(Suppl 1):S96
27. Yu C, Hernandez T, Zheng H, Yau SC, Huang HH, He RL, Yang J, and Yau SST. (2013) Real time classification of Viruses in 12 Dimensions. PLoS One, 8(5): e17293.
28. Huang HH and Yeh YR. (2011) Iterative algorithm for robust kernel principal component analysis. Neurocomputing, 74(18), 3921-3930
29. Huang HH, Hsiao CK and Huang SY. (2010) Statistics: Nonlinear regression. International Encyclopedia of Education, 3rd Edition, London, Elsevier. 339-346
Technical Reports
1. Huang HH, Hsiao CK, Huang SY. (2008) Nonlinear regression analysis. Academia Sinica Technical Report, 2008-8
2. Chen H and Huang HH. (2008) Model selection consistency of Cp-LASSO in linear regression with orthonormal regressors. Academia Sinica Technical Report, 2008-9
Submitted
1. Zhu Z, Sofa H, Kao CM and Huang HH (2023) A statistical reconstruction algorithm for positronium lifetime imaging using time-of-flight positron emission tomography.
2. Wang SH, Bai R, and Huang HH (2023) Mixed-type Multivariate Bayesian Sparse Variable Selection with Shrinkage Priors.
3. T Lu, C Chen, Huang HH, P Kochunov, E Hong, S Chen (2024) Multiple Imputation Method for High-Dimensional Neuroimaging Data.
Courses
- STA6714 Data Preparation – Spring 2024
- STA7348 Bayesian Modeling and Computation – Spring 2024, 2023, 2021, 2020
- STA4241 Statistical Learning – Fall 2023, 2022
- STA7734 Statistical Asymptotic Theory in Big Data – Fall 2023, 2022, 2020
- STA5505 Categorical Data Methods – Summer 2023, 2020
- STA6223 Conventional Survey Methods – Spring 2023
- STA4241 Statistical Learning – Fall 2023, 2022
- STA 5104 – Advanced Computer Processing of Statistical Data – Summer 2022, 2021
- STA6704 Data Mining Methodology II – Spring 2020