Jie Shen (jshen15)

Jie Shen

Assistant Professor

Charles V. Schaefer, Jr. School of Engineering and Science

Research

My research interests lie in both the theoretical aspects and applications of machine learning. I am particularly interested in the following problems and their interplay:

label-efficient learning, e.g. active learning;

noise-tolerant learning from unreliable data;

large-scale optimization, e.g. online and stochastic optimization;

high-dimensional statistics, e.g. low-rank matrix recovery and its applications to recommender systems and variable selection.

Experience

Visiting Scholar, Duke University, 2018
Graduate Research Assistant, Rutgers University, 2014 - 2018
Visiting Scholar, National University of Singapore, 2013 - 2014
Graduate Research Assistant, Shanghai Jiao Tong University, 2011 - 2013

Institutional Service

  • SES Working Group on Core AI Graduate Curriculum​ Member
  • Faculty search committee Member
  • SIAI faculty mentor for the Industrial Alliance Program Member
  • Faculty search Member
  • Computer Science Department Member
  • Computer Science Member
  • Computer Science Member
  • Computer Science Member
  • Computer Science Member

Professional Service

  • Neural Information Processing Systems Area Chair
  • Asian Conference on Machine Learning Area Chair
  • Visual Intelligence Associate Editor
  • International Conference on Machine Learning Area Chair
  • International Conference on Learning Representations Area Chair
  • Asian Conference on Machine Learning 2023 Area Chair
  • NeurIPS 2023 Emergency reviewer
  • ICML 2023 Program committee member
  • Electronic journal of statistics Reviewer
  • IEEE Trans. on Information Theory Reviewer
  • NeurIPS 2022 Program committee member
  • ACML 2022 Program committee member
  • ACML 2022 Area chair
  • IEEE Trans. on Information Theory Journal reviewer
  • ICML 2022 Program committee member
  • AISTATS 2022 Program committee member
  • ACML 2021 Program committee member
  • IEEE Trans. on PAMI Journal reviewer
  • NeurIPS 2021 Program committee member
  • ICML 2021 Program committee member
  • Machine Learning Journal Journal reviewer
  • ICLR 2021 Program committee member
  • AISTATS 2021 Program committee member
  • AAAI 2021 Program committee member
  • AAAI 2020 PC member
  • Journal of Machine Learning Research Reviewer
  • ACML 2020 PC member
  • NeurIPS 2020 PC member
  • IEEE Trans. on Signal Processing Reviewer
  • ICML 2020 PC member
  • IEEE Signal Processing Letters Reviewer
  • AISTATS 2020 PC member
  • Information and Inference: A Journal of the IMA Reviewer
  • International Conference on Machine Learning Program Committee Member
  • Artificial Intelligence and Statistics Program Committee Member
  • Neural Information Processing Systems Program Committee Member

Consulting Service

Facebook

Appointments

Assistant Professor, Stevens Institute of Technology, 2018 - present

Honors and Awards

CAREER, CRII

Professional Societies

  • ACM Member
  • ACM Member
  • IEEE Member
  • AAAS Member
  • IMS – Institute of Mathematical Statistics Member

Grants, Contracts and Funds

CAREER, $590,000, Solo PI
CRII, $175,000, Solo PI

Selected Publications

Conference Proceeding

  1. Wu, K.; Shen, J.; Ning, Y. N.; Wang, T.; Wang, H. (2023). Certified Graph Edge Unlearning via Influence Functions. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  2. Zeng, S.; Shen, J. (2023). Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise. International Conference on Machine Learning.
  3. Shen, J. (2023). PAC Learning of Halfspaces with Malicious Noise in Nearly Linear Time. International Conference on Artificial Intelligence and Statistics.
  4. Zeng, S.; Shen, J. (2023). Semi-Verified PAC Learning from the Crowd. International Conference on Artificial Intelligence and Statistics.
  5. Zeng, S.; Shen, J. (2022). List-Decodable Sparse Mean Estimation. NeurIPS 2022.
  6. Zeng, S.; Shen, J. (2022). Efficient PAC Learning from the Crowd with Pairwise Comparisons. ICML 2022.
  7. Shen, J.; Cui, N.; Wang, J. (2022). Metric-Fair Active Learning. ICML 2022.
  8. Zhu, T.; Shen, J. (2022). Residual-Based Sampling for Online Robust PCA. ICML 2022.
  9. Shen, J. (2021). On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise. ICML 2021.
  10. Shen, J. (2021). Sample-Optimal PAC Learning of Halfspaces with Malicious Noise. ICML 2021.
  11. Shen, J.; Zhang, C. (2021). Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance. Algorithmic Learning Theory.
  12. Zhang, C.; Shen, J.; Awasthi, P. (2020). Efficient Active Learning of Sparse Halfspaces with Arbitrary Bounded Noise. NeurIPS.
  13. Shen, J. (2020). One-Bit Compressed Sensing via One-Shot Hard Thresholding. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence.
    http://proceedings.mlr.press/v124/shen20b.html.

Journal Article

  1. Wang, J.; Shen, J.; Ma, X.; Arnold, A. (2023). Uncertainty-Based Active Learning for Reading Comprehension. Transactions on Machine Learning Research.
  2. Wang, J.; Shen, J. (2022). Fast Spectral Analysis for Approximate Nearest Neighbor Search. Machine Learning.

Courses

CS541, CS560