About Me

I obtained my bachelor's and doctoral degrees in computer science from Zhejiang University in 2011 and 2016. During my doctoral study, I was supported by "Microsoft Research Asia Fellowship" and "Baidu Scholarship", which were the highest fellowships/scholarships in China. From 2016 to 2018, I was a postdoc scholar at the Department of Statistics, UC Berkeley. From 2018 to 2021, I was a tenure-track assistant professor at the Department of Computer Science, Stevens Institute of Technology. From 2021 to 2023, I worked at Xiaohongshu (Shanghai) as an ML engineer and manager.

My expertise includes search engines, machine learning, reinforcement learning, and numerical algorithms. I also have experience in NLP and recommender systems. When I was in academia, I did research on machine learning, numerical optimization, parallel computing, etc. In the industry, I lead a team working on search ranking, search retrieval, and NLP techniques. We have launched over 10 projects that significantly improved key indicators such DAU, retention, CTR, etc. In my spare time, I published a book Deep Reinforcement Learning (in Chinese), taught an open course Industrial Recommender System (in Chinese), and wrote a book draft Search Engine (in Chinese)

Experience

Xiaohongshu (Shanghai), 09/2021---07/2023

machine learning engineer and manager

Department of Computer Science, Stevens Institute of Technology, 09/2018---12/2021

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].

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Last update: 2023-08-06