About Me

I am a tenure-track assistant professor at the Department of Computer Science, Stevens Institute of Technology. From 2016 to 2018, I was postdoc scholar at Department of Statistics, UC Berkeley. I worked with Michael Mahoney. In 2011 and 2016, I got both of my doctoral and bachelor's degrees from Zhejiang University, China. My advisor was Zhihua Zhang. During my doctoral study, I have been supported by "the Microsoft Research Asia Fellowship" and "Baidu Scholarship", which were the highest fellowships/scholarships in China.

To prospective students:
- I will take one or two PhD students and offer full financial support. Drop me a line if you would like to apply.
- I will NOT offer RA position to master student.
- I may take Stevens undergraduates who have high GPA and want to start research training early. But I am unable to provide financial support.

Research Interest

Machine learning

Computational methods such as numerical optimization, matrix computation, bootstrap, etc.

Statistical machine learning, ensemble methods, regression, clustering, dimensionality reduction, kernel methods, Bayesian methods.

Randomized numerical linear algebra

Matrix sketching, randomized matrix computation, kernel approximation, etc.

Distributed computing

The design, analysis, and implementation of distributed algorithms (for machine learning and optimization).

Major Experience

Department of Computer Science, Stevens Institute of Technology, from 09/2018

tenure-track assistant professor

Department of Statistics, UC Berkeley, 07/2016---06/2018

postdoc researcher, with Michael Mahoney

Zhejiang University, Doctor of Engineering, 09/2011---06/2016

College of Computer Science and Techonology

Zhejiang University, Bachelor of Engineering, 08/2007---07/2011

College of Computer Science and Techonology

Chu Kochen Honors College

Graduate with "the 100 Best Bachelor Theses Award"

Representative Papers [Full List] [Google Scholar]

  • Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds.
    Shusen Wang, Alex Gittens, and Michael W. Mahoney.
    Accepted by Journal of Machine Learning Research (JMLR), 2018.
    [pdf] [arXiv:1706.02803]

  • Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging.
    Shusen Wang, Alex Gittens, and Michael W. Mahoney.
    Journal of Machine Learning Research (JMLR), 18(218):1-50, 2018.
    [pdf] [bib]

  • GIANT: Globally Improved Approximate Newton Method for Distributed Optimization.
    Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, and Michael W. Mahoney.
    In 32nd Conference on Neural Information Processing Systems (NIPS), 2018.
    [pdf] [bib] [long version] [Spark Code].

  • Improving CUR Matrix Decomposition and the Nystrom Approximation via Adaptive Sampling.
    Shusen Wang and Zhihua Zhang.
    Journal of Machine Learning Research (JMLR), 14: 2729-2769, 2013.
    [pdf] [bib]

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Last update: 2019-01-06