Yue Ning

Assistant Professor

School: School of Engineering and Science

Department: Computer Science

Building: Gateway Center

Room: S448

Phone: (201) 216-5486

Email: yning5@stevens.edu

Website

Education
  • PhD (2018) Virginia Tech (Computer Science)
Research

Applied Machine Learning
Text Mining and Knowledge Discovery
Social Media Analysis and Personalization

Institutional Service
  • Data Science Committee Member
  • Graduate advising Member
  • CS Seminar Member
  • Faculty Hiring Committee Member
Professional Service
  • IEEE Transactions on Neural Networks and Learning Systems Reviewer
  • IEEE Transactions on Emerging Topics in Computational Intelligence Review
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining Program Committee Member
  • NSF NSF panelist
  • International Conference on Machine Learning (ICML) Program Committee Member
  • IEEE Transactions on Knowledge and Data Engineering (TKDE) Reviewer
  • Pacific-Asia Conference on Knowledge Discovery and Data Mining Program Committee Member
  • AAAI Conference on Artificial Intelligence Program Committee Member
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining Program Committee
  • SIAM International Conference on Data Mining Program Committee
Professional Societies
  • AAAI – Association for the Advancement of Artificial Intelligence Member
  • ACM – Association for Computing Machinery Member
Grants, Contracts, and Funds

NSF IIS 1948432: CRII: III: Learning Dynamic Graph-based Precursors for Event Modeling

Selected Publications
Conference Proceeding
  1. Wang, H.; Liu, R.; Ning, Y.; Wu, Y. (2020). Fairness of Classification Using Users’ Social Relationships in Online Peer-To-Peer Lending, FATES (Fairness, Accountability, Transparency, Ethics and Society) on the Web, joint with the Web Conference 2020 proceeding, 733-742. FATES (Fairness, Accountability, Transparency, Ethics and Society) on the Web, joint with the Web Conference 2020 proceeding.
  2. Chen, Y.; Ning, Y.; Slawski, M.; Rangwala, H. (2020). Asynchronous Online Federated Learning for EdgeDevices with Non-IID Data. Proceedings of 2020 IEEE International Conference on Big Data. IEEE Big Data.
    https://arxiv.org/abs/1911.02134.
  3. 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.
  4. Chen, Y.; Ning, Y.; Chai, Z.; Rangwala, H. (2020). Federated Multi-task Hierarchical Attention Model for Sensor Analytics. 2020 International Joint Conference on Neural Networks (IJCNN). Glasgow, Scotland: IEEE WCCI - IJCNN.
  5. Ning, Y.; Vaidya, A.; Mai, F. (2020). Empirical Analysis of Multi-Task Learning for Reducing Identity Bias in Toxic Comment Detection. 14th International Conference on Web and Social Media (ICWSM). Atlanta, Georgia: AAAI ICWSM.
    https://www.aaai.org/ojs/index.php/ICWSM/article/view/7334/7188.
  6. Hui, W.; Li , Y.; Ning, Y.; Liu, R.; Wu, Y. (2020). Fairness of Classification Using Users' Social Relationships in Online Peer-To-Peer Lending. (pp. 733-742). Hoboken: Proceeding of WWW conference, 2020.
  7. Deng, S.; Rangwala, H.; Ning, Y. (2019). Learning Dynamic Context Graphs for Predicting Social Events. Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . Anchorage, Alaska: ACM SIGKDD.
Tutorial
  1. Ning, Y.; Zhao, L.; Chen, F.; Lu, C.; Rangwala, H. (2019). Spatio-temporal Event Forecasting and Precursor Identification. Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York: ACM.
Courses

CS559 Machine Learning
CS584 Natural Language Processing