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 a 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, where I worked with my advisor Zhihua Zhang. During my doctoral study, I was supported by "Microsoft Research Asia Fellowship" and "Baidu Scholarship", which are (or at least were) the highest fellowships/scholarships in China.

Research Interest

Machine learning

Communication efficiency and security in federated learning.

Multi-agent reinforcement learning.

Numerical algorithms

Randomized numerical linear algebra, matrix sketching, efficient matrix computation.

Numerical optimization, distributed optimization.

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

Representative Papers [Full List] [Google Scholar]

  • A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication.
    Miles E. Lopes, Shusen Wang, Michael W. Mahoney.
    Journal of Machine Learning Research (JMLR), 20(39):1-40, 2019.
    [pdf] [bib] [arXiv:1708.01945].

  • Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds.
    Shusen Wang, Alex Gittens, and Michael W. Mahoney.
    Journal of Machine Learning Research (JMLR), 20(12):1-49, 2019.
    [pdf] [bib] [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.
    A short version has appeared in ICML 2017. (There are errors in the ICML version; please refer to the journal version for the correct results.)
    [pdf] [bib]

  • Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition.
    Shusen Wang, Zhihua Zhang, and Tong Zhang.
    Journal of Machine Learning Research (JMLR), 17(210):1−49, 2016.
    [pdf] [bib]

  • SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions.
    Shusen Wang, Luo Luo, and Zhihua Zhang.
    Journal of Machine Learning Research (JMLR), 17(49):1-49, 2016.
    Short versions have appeared in AISTATS 2014 and KDD 2014.
    [pdf] [bib]

  • 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.
    A short version has appeared in NIPS 2012.
    [pdf] [bib]

  • EP-GIG Priors and Applications in Bayesian Sparse Learning.
    Zhihua Zhang, Shusen Wang, Dehua Liu, and Michael I. Jordan.
    Journal of Machine Learning Research (JMLR), 13: 2031-2061, 2012.
    [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].

  • Communication-Efficient Distributed SVD via Local Power Iterations.
    Xiang Li, Shusen Wang, Kun Chen, and Zhihua Zhang.
    arXiv:2002.08014, 2020.

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Last update: 2021-02-19