Shusen Wang

Assistant Professor

School: School of Engineering and Science

Department: Computer Science

Building: Gateway Center

Room: S354

Phone: (201) 216-5485

Email: swang134@stevens.edu

Website

Research

Deep learning, reinforcement learning, parallel computing, statistical machine learning, numerical optimization, randomized algorithms.

Experience

2018-present: Assistant Professor, Department of Computer Science, Stevens Institute of Technology, USA.

2016-2018: Postdoc, Department of Statistics, UC Berkeley, USA.

Institutional Service
  • Faculty Search Committee 2019 Member
Professional Service
  • Journal of Machine Learning Research Journal reviewer
  • Statistica Sinica Journal reviewer
  • SIGKDD Conference on Knowledge Discovery and Data Mining conference committee member
  • Uncertainty in Artificial Intelligence (UAI) conference committee member
  • International Joint Conference on Artificial Intelligence (IJCAI) conference committee member
  • IEEE Transactions on Pattern Analysis and Machine Intelligence Journal reviewer
  • Springer Book reviewer
  • International Conference on Artificial Intelligence and Statistics (AISTATS) conference committee member
  • AAAI Conference on Artificial Intelligence conference committee member
  • SIAM Journal on Matrix Analysis and Applications Journal reviewer
  • Supercomputing 2019 Conference Committee member
  • IEEE Transactions on Pattern Analysis and Machine Intelligence Journal Reviewer
  • Journal of Machine Learning Research Journal Reviewer
  • Conference on Uncertainty in Artificial Intelligence Conference Committee member
  • International Joint Conferences on Artificial Intelligence Conference Committee member
  • International Conference on Machine Learning Conference Committee member
  • IEEE Transactions on Information Theory Journal Reviewer
  • Neurocomputing Journal Reviewer
  • International Conference on Artificial Intelligence and Statistics (AISTATS 2018) Conference committee member
  • Journal of Machine Learning Research Journal Reviewer
  • IEEE Transaction on Signal Processing Journal Reviewer
Selected Publications
Conference Proceeding
  1. Li, X.; Wang, S.; Chen, K.; Zhang, Z. (2021). Communication-efficient distributed SVD via local power iterations (pp. 6504--6514). International Conference on Machine Learning.
  2. Peng, H.; Huang, S.; Geng, T.; Li, A.; Jiang, W.; Liu, H.; Wang, S.; Ding, C. (2021). Accelerating Transformer-based Deep Learning Models on FPGAs using Column Balanced Block Pruning.. International Symposium on Quality Electronic Design.
  3. Zhang, M.; Wang, S. (2021). Matrix Sketching for Secure Collaborative Machine Learning. (pp. 6504--6514). International Conference on Machine Learning.
  4. Deng, S.; Wang, S.; Ning, Y. (2020). Cola-GNN: Cross-location Attention based Graph Neural Networks for Long-term ILI Prediction. Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM). ACM CIKM.
  5. Li, X.; Huang, K.; Yang, W.; Wang, S.; Zhang, Z. (2020). On the Convergence of FedAvg on Non-IID Data.. International Conference on Learning Representations (ICLR).
  6. Li, X.; Wang, S.; Zhang, Z. (2020). Do Subsampled Newton Methods Work for High-Dimensional Data?. AAAI Conference on Artificial Intelligence (AAAI). Association for the Advancement of Artificial Intelligence (AAAI).
  7. Wang, S. (2019). A Sharper Generalization Bound for Divide-and-Conquer Ridge Regression.. In AAAI Conference on Artificial Intelligence.
  8. Gupta, V.; Wang, S.; Courtade, T.; Ramchandran, K. (2018). OverSketch: Approximate Matrix Multiplication for the Cloud. IEEE International Conference on Big Data. IEEE.
  9. Wang, S.; Roosta-Khorasani, F.; Xu, P.; Mahoney, M. W. (2018). GIANT: Globally Improved Approximate Newton Method for Distributed Optimization (vol. 32). In 32nd Conference on Neural Information Processing Systems.
Journal Article
  1. Li, B.; Wang, S.; Zhang, J.; Cao, X.; Zhao, C. (2021). Fast Randomized-MUSIC for Mm-Wave Massive MIMO Radars. IEEE Transactions on Vehicular Technology (2 ed., vol. 70, pp. 1952--1956). IEEE.
  2. Li, B.; Wang, S.; Feng, Z.; Zhang, J.; Cao, X.; Zhao, C. (2021). Fast pseudo-spectrum estimation for automotive massive MIMO radar. IEEE Internet of Things Journal. IEEE.
  3. Li, B.; Wang, S.; Zhang, J.; Cao, X.; Zhao, C. (2020). Randomized Approximate Channel Estimator in Massive-MIMO Communication. IEEE Communications Letters. IEEE.
    https://ieeexplore.ieee.org/document/9115654.
  4. Lopes, M. E.; Wang, S.; Mahoney, M. W. (2019). A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication. Journal of Machine Learning Research (39 ed., vol. 20, pp. 1-40).
  5. Wang, S.; Gittens, A.; Mahoney, M. W. (2019). Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds. Journal of Machine Learning Research (12 ed., vol. 20, pp. 1-49).