
Yue Ning
Assistant Professor
Charles V. Schaefer, Jr. School of Engineering and Science
Department of Computer Science
Education
- PhD (2018) Virginia Tech (Computer Science)
Research
Applied Machine Learning
Text Mining and Knowledge Discovery
Social Media Analysis and Personalization
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
NSF CAREER: Towards Deep Interpretable Predictions for Multi-Scope Temporal Events
Selected Publications
Conference Proceeding
- 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. - 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. - 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. - 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. - 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. - 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.
- Lu, C.; Reddy, C. K.; Chakraborty, P.; Kleinberg, S. N.; Ning, Y. N. (2021). Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare. IJCAI.
- 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.
- 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.
- 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. - 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. - 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.
- 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.
- 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. - 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.
- 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
- 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. - 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. - 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
- 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. - 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
CS584 Natural Language Processing