Yue Ning (yning5)

Yue Ning

Assistant Professor

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 Chair
  • Graduate advising Member
  • Faculty Search Committee Chair
  • Data Science committee Member
  • Data Science Committee Member
  • Graduate advising Member
  • CS Seminar Member
  • Faculty Hiring Committee Member

Professional Service

  • Academy for Technology and Computer Science (ATCS) at Bergen County Academics (BCA) Advisory board
  • ACM SIGKDD 2022 Student Travel Award Co-chair
  • ASONAM 2022 Program committee member
  • CIKM 2022 program committee member
  • NeurIPS 2022 Program committee member
  • NSF Panelist
  • ACM SIGKDD 2022 program committee member
  • IJCAI 2022 program committee member
  • SDM 2022 program committee member
  • AAAI Conference on Artificial Intelligence (AAAI 2022) program committee member
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021) Program Committee Member
  • IJCAI 2021 program committee member
  • ICML 2021 program committee member
  • AAAI Conference on Artificial Intelligence (AAAI 2021) program committee member
  • NSF Panelist
  • 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 (KDD2020) Program Committee Member
  • NSF Panelist
  • International Conference on Machine Learning (ICML 2020) Program Committee Member
  • IEEE Transactions on Knowledge and Data Engineering (TKDE) Reviewer
  • Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020) Program Committee Member
  • AAAI Conference on Artificial Intelligence (AAAI 2020) Program Committee Member
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019) Program Committee
  • SIAM International Conference on Data Mining (SDM 2019) Program Committee

Professional Societies

  • ACM – Association for Computing Machinery Member
  • IEEE Member
  • AAAI – Association for the Advancement of Artificial Intelligence Member

Grants, Contracts and Funds

NSF IIS 1948432: CRII: III: Learning Dynamic Graph-based Precursors for Event Modeling
NSF CAREER: Towards Deep Interpretable Predictions for Multi-Scope Temporal Events

Selected Publications

Conference Proceeding

  1. Deng, S.; Rangwala, H.; Ning, Y. (2022). Robust Event Forecasting with Spatiotemporal Confounder Learning. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . ACM SIGKDD.
    https://dl.acm.org/doi/abs/10.1145/3534678.3539427.
  2. Lu, C.; Han, T.; Ning, Y. (2022). Context-aware health event prediction via transition functions on dynamic disease graphs. Proceedings of the AAAI Conference on Artificial Intelligence (4 ed., vol. 36, pp. 4567-4574). AAAI.
    https://www.aaai.org/AAAI22Papers/AAAI-6800.LuC.pdf.
  3. Huang, J.; Ning, Y.; Nie, D.; Guan, L.; Jia, X. (2022). Weakly-supervised Metric Learning with Cross-Module Communications for the Classification of Anterior Chamber Angle Images. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 752-762). IEEE/CVF CVPR.
    https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Weakly-Supervised_Metric_Learning_With_Cross-Module_Communications_for_the_Classification_of_CVPR_2022_paper.pdf.
  4. Li, J.; Ning, Y. (2022). Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation Inference. Proceedings of the 16th International AAAI Conference on Web and Social Media (vol. 16, pp. 607-617). AAAI ICWSM.
    https://ojs.aaai.org/index.php/ICWSM/article/download/19319/19091.
  5. Li, Y.; Wang, X.; Ning, Y.; Wang, H. (2022). FairLP: Towards Fair Link Prediction on Social Network Graphs. Proceedings of the International AAAI Conference on Web and Social Media (vol. 16, pp. 628-639). AAAI ICWSM.
    https://ojs.aaai.org/index.php/ICWSM/article/download/19321/19093.
  6. Ning, Y.; Deng, S.; Rangwala, H. (2021). Understanding Event Predictions via Contextualized Multilevel Feature Learning. Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 342-351). ACM CIKM.
  7. Lu, C.; Reddy, C. K.; Chakraborty, P.; Kleinberg, S.; Ning, Y. (2021). Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare. IJCAI.
  8. Wu, K.; Yuan, X.; Ning, Y. (2021). Incorporating Relational Knowledge in Explainable Fake News Detection. Pacific-Asia Conference on Knowledge Discovery and Data Mining. PAKDD.
  9. 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.
  10. 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.
  11. 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. ACM CIKM.
    https://dl.acm.org/doi/10.1145/3340531.3411975.
  12. Deng, S.; Rangwala, H.; Ning, Y. (2020). Dynamic Knowledge Graph based Multi-Event Forecasting.. ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM SIGKDD.
  13. 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.
  14. Vaidya, A.; Mai, F.; Ning, Y. (2020). Empirical Analysis of Multi-Task Learning for Reducing Identity Bias in Toxic Comment Detection. Proceedings of the 14th AAAI International Conference on Web and Social Media (ICWSM). Atlanta, Georgia: AAAI ICWSM.
    https://www.aaai.org/ojs/index.php/ICWSM/article/view/7334/7188.
  15. 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.
  16. 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.
    https://dl.acm.org/doi/10.1145/3292500.3330919.

Journal Article

  1. Xu, J.; Xiao, Y.; Wang, H.; Ning, Y.; Shenkman, E. A.; Bian, J.; Wang, F. (2022). Algorithmic Fairness in Computational Medicine.. eBioMedicine, Part of THE LANCET Discovery Science. THE LANCET.
    https://www.sciencedirect.com/science/article/pii/S2352396422004327.
  2. Kim, R.; Ning, Y. (2022). Recurrent Multi-task Graph Convolutional Networks for COVID-19 Knowledge Graph Link Prediction. Springer Journal of Communications in Computer and Information Science (vol. 1512, pp. 411-419). Springer.
    https://link.springer.com/chapter/10.1007/978-3-030-96498-6_24.
  3. Lu, C.; Reddy, C. K.; Ning, Y. (2021). Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction. IEEE Transactions on Cybernetics (pp. 1-13). IEEE.
    https://ieeexplore.ieee.org/document/9543467.

Tutorial

  1. Ning, Y.; Deng, S.; Rangwala, H. (2021). Explainable AI for Societal Event Predictions: Foundations, Methods, and Applications. AAAI 2021. AAAI 2021.
    https://yue-ning.github.io/aaai-21-tutorial.html.
  2. 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