Boxiang Wang
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Welcome to my home page! I am an Assistant Professor in the Department of Statistics and Actuarial Science and a Center Faculty at Center for Advancing Multimorbidity Science at The University of Iowa. I obtained my Ph.D. in the School of Statistics at the University of Minnesota under the supervision of Professor Hui Zou. I got my master's degree in the Department of Applied Statistics and Operation Research at the Bowling Green State University, and my bachelor's degree in the School of Mathematics at Nankai University. My main research focus is at the crossroads of statistics, machine learning, and optimization. I specialize in supervised learning algorithms such as support vector machines, unsupervised learning algorithms including model-based clustering, model assessment, tensor data analysis, and optimal experimental design. I am also interested in actuarial science and insurance applications. |
Address: 261 Schaeffer Hall, Iowa City, IA 52242
Phone: 319-335-2294
E-mail: boxiang-wang at uiowa.edu
Publications
Lian, Y., Yang, A.Y., Wang, B., Shi, P., and Platt, R. W. (2023) "A Tweedie compound Poisson model in reproducing kernel Hilbert space", Technometrics, in press.
Zhou, L., Wang, B. and Zou, H. (2023) "Sparse convoluted rank regression in high dimensions", Journal of the American Statistical Association, in press.
Wang, B., Zhou, L, Yang, J., and Mai, Q. (2023) "Density-convoluted tensor support vector machines", Statistics and Its Interface, in press.
Wang, B., Zhou, L, Gu, Y., and Zou, H. (2023) "Density-convoluted support vector machines for high-dimensional classification", IEEE Transactions on Information Theory, 69(4), 2523-2536.
Wang, B. and Yang, A.Y. (2022) "A consolidated cross-validation algorithm for support vector machines via data reduction", Advances in Neural Information Processing Systems, 35, 1-12.
Wang, B. and Zou, H. (2022) "Fast and exact cross-validation theory for large-margin classification", Technometrics, 61(3), 291-298.
Wang, B. and Zou, H. (2021) "Honest leave-one-out cross-validation for estimating post-tuning generalization error", Stat, 10(1), e413.
Hao, B., Wang, B., Zhang, J., Wang, P., Yang, J., and Sun, W.W. (2021) "Sparse tensor additive regression", Journal of Machine Learning Research, 22(64), 1-43.
Wang, B. and Zou, H. (2019) "A multicategory kernel distance weighted discrimination method for multiclass classification", Technometrics, 61(3), 396-408.
Wang, B. and Zou, H. (2018) "Another look at distance-weighted discrimination", Journal of the Royal Statistical Society, Series B, 80(1), 177-198.
Koerner, T.K., Zhang, Y., Nelson, P., Wang, B., and Zou, H. (2017) "Neural indices of phonemic discrimination and sentence-level speech intelligibility in quiet and noise: A P3 study", Hearing Research, 350, 58-67.
Wang, B. and Zou, H. (2016) "Sparse distance weighted discrimination", Journal of Computational and Graphical Statistics, 25(3), 826-838.
Koerner, T.K., Zhang, Y., Nelson, P., Wang, B., and Zou, H. (2016) "Neural indices of phonemic discrimination and sentence-level speech intelligibility in quiet and noise: A mismatch negativity study", Hearing Research, 339, 40-49.
Ning, W., Yeh, A., Wu, X., and Wang, B. (2015) "A nonparametric phase I control chart for individual observations based on empirical likelihood ratio", Quality and Reliability Engineering International, 31(1), 37-55.
Software
R package: ktweedie 
The R package fits kernel-based 'Tweedie' compound Poisson gamma models using high-dimensional predictors for the analyses of zero-inflated response variables. The package features built-in estimation, prediction and cross-validation tools and supports choice of different kernel functions.
Download from CRAN: https://cran.r-project.org/web/packages/ktweedie
R package: kerndwd 
The R package kerndwd uses the majorization-minimization principle to solve the linear DWD. It also formulates the kernel DWD in an reproducing kernel Hilbert space and develops the same algorithm for linear DWD. The package involves very fast tuning procedures and delivers prediction accuracy that is highly comparable as the kernel SVM.
Download from CRAN: https://cran.r-project.org/web/packages/kerndwd
R package: sdwd 
The R package sdwd uses coordinate descent to solve sparse distance weighted discrimination for high-dimensional classification. The package computes the entire solution path for lasso, elastic net, and adpative lasso/elastic net penalites. The implementation is efficient involving computational tricks such as strong rule, warm start, and active set. The R package sdwd is extremely fast, as compared with some sparse support vector machines.
Download from CRAN: https://cran.r-project.org/web/packages/sdwd
R package: CUSUMdesign 
The R package CUSUMdesign employs the Markov chain algorithm to compute the average run length and the decision interval when the CUSUM charts are designed. The CUSUM chart is widely used for detecting small but persist shifts in statistical process control.
Download from CRAN: https://cran.r-project.org/web/packages/CUSUMdesign
Honors & Awards
Deans' Micro Research Grants, University of Iowa, 2020
Travel Support for Symposium on Data Science and Statistics, 2019
Student Paper Award, Section of SLDM, Joint Statistical Meetings, 2016
Doctoral Dissertation Fellowship, University of Minnesota, 2016
Bernard W Lindgren Teaching Award, University of Minnesota, 2016
Martin-Buehler Fellowship, University of Minnesota, 2015
Alumni Fellowship, University of Minnesota, 2014
First Year Scholarship, University of Minnesota, 2012
James A. Sullivan Outstanding Graduate Award, BGSU, 2012
Robert A. Pattern Book Scholarship, BGSU, 2011