Boxiang Wang

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Welcome to my home page!

I am a Ph.D. Candidate, in the School of Statistics at the University of Minnesota, advised by Professor Hui Zou.

Prior to my Ph.D. studies, I got my master's degree in the Department of Applied Statistics and Operation Research at the Bowling Green State University in 2012, and my bachelor's degree in the School of Mathematics at Nankai University in 2010.

My research area is machine learning and big data with a specific focus on modern classification. My research also spans statistical computing, high-dimensional data analysis, experimental design and optimal design.

  • Address: 224 Church Street, 313 Ford Hall, Minneapolis, MN 55455

  • Phone: 567-213-1606

  • E-mail: boxiang at umn.edu

Honors & Awards

Publications

  1. Wang, B. and Zou, H. (2018) "Another look at distance-weighted discrimination", Journal of the Royal Statistical Society, Series B, 80(1), 177-198.

  2. Wang, B. and Zou, H. (2016) "Sparse distance weighted discrimination", Journal of Computational and Graphical Statistics, 25(3), 826-838.

  3. Wang, B. and Zou, H. (2017) "A multicategory kernel distance weighted discrimination method for multiclass classification", revision invited.

  4. 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.

  5. 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.

  6. 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.

Software

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.