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



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.

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.

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.

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.

Honors & Awards