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.
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
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
- 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
- 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
CRII, $175,000, Solo PI
Selected Publications
Conference Proceeding
- 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.
- Zeng, S.; Shen, J. (2023). Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise. International Conference on Machine Learning.
- Shen, J. (2023). PAC Learning of Halfspaces with Malicious Noise in Nearly Linear Time. International Conference on Artificial Intelligence and Statistics.
- Zeng, S.; Shen, J. (2023). Semi-Verified PAC Learning from the Crowd. International Conference on Artificial Intelligence and Statistics.
- Zeng, S.; Shen, J. (2022). List-Decodable Sparse Mean Estimation. NeurIPS 2022.
- Zeng, S.; Shen, J. (2022). Efficient PAC Learning from the Crowd with Pairwise Comparisons. ICML 2022.
- Shen, J.; Cui, N.; Wang, J. (2022). Metric-Fair Active Learning. ICML 2022.
- Zhu, T.; Shen, J. (2022). Residual-Based Sampling for Online Robust PCA. ICML 2022.
- Shen, J. (2021). On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise. ICML 2021.
- Shen, J. (2021). Sample-Optimal PAC Learning of Halfspaces with Malicious Noise. ICML 2021.
- Shen, J.; Zhang, C. (2021). Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance. Algorithmic Learning Theory.
- Zhang, C.; Shen, J.; Awasthi, P. (2020). Efficient Active Learning of Sparse Halfspaces with Arbitrary Bounded Noise. NeurIPS.
- 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
- Wang, J.; Shen, J.; Ma, X.; Arnold, A. (2023). Uncertainty-Based Active Learning for Reading Comprehension. Transactions on Machine Learning Research.
- Wang, J.; Shen, J. (2022). Fast Spectral Analysis for Approximate Nearest Neighbor Search. Machine Learning.
Courses
CS541, CS560