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
- SES Working Group on PhD Recruitment and Lab Culture Member
- SIAI steering committee Member
- Research and Entrepreneurship Committee Member
- Data Science Committee Member
- Stevens Institute for Artificial Intelligence (SIAI) director search committee Member
- Data Science committee Chair
- Graduate advising Member
Professional Service
- AAAI Conference on Artificial Intelligence (AAAI) Doctoral Consortium Chair
- AAAI Conference on Artificial Intelligence (AAAI) Program Committee Member
- International Conference on Learning Representations (ICLR) Program Committee Member
- IEEE BigData Program Committee Member
- SIAM International Conference on Data Mining (SDM) Program Committee Member
- Conference on Neural Information Processing Systems (NeurIPS) Program Committee Member
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) Area Chair
- ACM Conference on Information and Knowledge Management (CIKM) Program Committee Member
- National Science Foundation (NSF) Panelist
- International Joint Conferences on Artificial Intelligence (IJCAI) Program Committee Member
- International Conference on Machine Learning (ICML) Program Committee Member
- The Web Conference (TheWebConf, previously WWW) Program Committee Member
- IEEE International Conference on Computer Communications (INFOCOM) Local Arrangement Chair
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) Program Committee Member
- IEEE/ACM Transactions on Computational Biology and Bioinformatic (TCBB) Reviewer
- IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Program Committee Member
- Sensors Reviewer
- Academy for Technology and Computer Science (ATCS) at Bergen County Academics (BCA) Advisory board
- ACM SIGKDD 2022 Student Travel Award Co-chair
- IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Program Committee Member
- Frontiers in Big Data Editor
- Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) Program Committee Member
- Nature Communications Reviewer
- Pattern Recognition Reviewer
- IEEE Transactions on Neural Networks and Learning Systems (TNNLS) Reviewer
- IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) Review
- IEEE Transactions on Knowledge and Data Engineering (TKDE) Reviewer
- IEEE Transactions on Intelligent Transportation System Reviewer
- ACM Transactions on Knowledge Discovery from Data (TKDD) Reviewer
Professional Societies
- ACM – Association for Computing Machinery Member
- IEEE – Institute of Electrical and Electronics Engineers 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.; de Rijke , M.; Ning, Y. (2024). Advances in Human Event Modeling: From Graph Neural Networks to Language Models. ACM SIGKDD.
- Lu, C.; Reddy, C. K.; Wang, P.; Ning, Y. (2023). Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning. Proceedings of the 37th Conference on Neural Information Processing Systems. NeurIPS.
- 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.
- Chen, J.; Creamer, G.; Ning, Y. (2022). Forecasting Emerging Pandemics with Transfer Learning and Location-aware News Analysis. IEEE BigData. IEEE BigData.
- Deng, S.; Rangwala, H.; Ning, Y. (2022). Causality Enhanced Societal Event Forecasting With Heterogeneous Graph Learning. Proceedings of the 22nd IEEE International Conference on Data Mining . ICDM 2022.
- Han, X.; Ning, Y. (2022). Text-enhanced Multi-Granularity Temporal Graph Learning for Event Prediction. Proceedings of the 22nd IEEE International Conference on Data Mining. ICDM 2022.
- Wu, K.; Erickson, J.; Wang, H.; Ning, Y. (2022). Equipping Recommender Systems with Individual Fairness via Second-order Proximity Embedding. International Conference on Social Networks Analysis and Mining. ACM Conference on Social Networks Analysis and Mining.
- 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.; Ning, Y. (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
- Chen, J.; Creamer, G.; Ning, Y.; Ben-Zvi, T. (2023). Healthcare Sustainability: Hospitalization Rate Forecasting with Transfer Learning and Location-Aware News Analysis. Sustainability. MDPI.
- Lu, C.; Reddy, C. K.; Wang, P.; Nie, D.; Ning, Y. (2023). Multi-Label Clinical Time-Series Generation via Conditional GAN. IEEE Transactions on Knowledge and Data Engineering. IEEE.
https://ieeexplore.ieee.org/abstract/document/10236560. - Hossain, K.; Harutyunyan, H.; Ning, Y.; Kennedy, B.; Ramakrishnan, N.; Galstyan, A. (2022). Identifying geopolitical event precursors using attention-based LSTMs. Frontiers in Artificial Intelligence.
- 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