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.
In late 2021, I joined 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) and taught an open course Industrial Recommender System (in Chinese).
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) and taught an open course Industrial Recommender System (in Chinese).
Experience
Xiaohongshu (Shanghai), from 12/2021
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]
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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].
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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]
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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]
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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]
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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]
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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]
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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]
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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].