Yue Ning

• PhD (2018) Virginia Tech (Computer Science)

• Task Force on Creation of Undergraduate AI Major and AI Minor Chair
• Communications and marketing committee Chair
• SIAI steering committee Member
• SES Working Group on PhD Recruitment and Lab Culture Member
• Data Science Committee Member
• Research and Entrepreneurship Committee Member
• Stevens Institute for Artificial Intelligence (SIAI) director search committee Member
• Data Science committee Chair
• Graduate advising Member

• International Conference on Learning Representations (ICLR) Program Committee Member
• IEEE BigData Program Committee Member
• AAAI Conference on Artificial Intelligence (AAAI) 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
• International Joint Conferences on Artificial Intelligence (IJCAI) Program Committee Member
• International Conference on Machine Learning (ICML) Program Committee Member
• AAAI Conference on Artificial Intelligence (AAAI) Doctoral Consortium Chair
• SIAM International Conference on Data Mining (SDM) Program Committee Member
• National Science Foundation (NSF) Panelist
• 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

• IEEE – Institute of Electrical and Electronics Engineers Member
• ACM – Association for Computing Machinery Member
• AAAI – Association for the Advancement of Artificial Intelligence Member

Conference Proceeding

1. Cui, N.; Wang, H.; Ning, Y. (2025). Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters. Proceedings of the 5th Workshop on Bias and Fairness in AI (BIAS) at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECMLPKDD.
2. Hu, P.; Fan, M.; Han, X.; Lu, C.; Zhang, W.; Kang, H.; Ning, Y.; Lu, D. (2025). AdaTrip: Adaptive Graph on Transformer for Multi-Reservoir Inflow Prediction. The 11th Workshop on Data Mining in Earth System Science (DMESS) at IEEE International Conference on Data Mining (ICDM). IEEE ICDM.
3. Scavotto, Z.; Han, X.; Ning, Y. (2025). Learning for Inflation Forecasting with Dynamic Feature Spaces. Undergraduate and High School Symposium at IEEE International Conference on Data Mining (ICDM). IEEE ICDM.
4. Han, X.; Hu, P.; Lu, C.; Ding, J.; Liu, F.; Ning, Y. (2025). No Black Boxes: Interpretable and Interactable Predictive Healthcare with Knowledge-Enhanced Agentic Causal Discovery. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. EMNLP.
5. He, C.; Zhang, W.; Chen, V.; Ning, Y.; Wang, P. (2025). Task as Context Prompting for Accurate Medical Symptom Coding Using Large Language Models. Proceedings of the IEEE/ACM conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE/ACM CHASE.
6. Han, X.; Rangwala, H.; Ning, Y. (2025). DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification. Proceedings of the 28th International Conference on Artificial Intelligence and Statistics. AISTATS.
7. Yang, E.; Hu, P.; Han, X.; Ning, Y. (2024). MPLite: Multi-Aspect Pretraining for Mining Clinical Health Records. Proceedings of the 2024 IEEE International Conference on Big Data Workshop on Big Data and AI for Healthcare.. IEEE BigData.
8. Sood, P.; He, C.; Gupta, D.; Ning, Y.; Wang, P. (2024). Understanding Student Sentiment on Mental Health Support in Colleges Using Large Language Models. Proceedings of the 2024 IEEE International Conference on Big Data. IEEE BigData.
   https://www.computer.org/csdl/proceedings-article/bigdata/2024/10826043/23ykBz54h6U.
9. Han, X.; Feng, Z.; Ning, Y. (2024). A Topology-aware Graph Coarsening Framework for Continual Graph Learning. Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS). NeurIPS.
   https://arxiv.org/abs/2401.03077.
10. Deng, S.; de Rijke , M.; Ning, Y. (2024). Advances in Human Event Modeling: From Graph Neural Networks to Language Models. ACM SIGKDD.
11. Cui, N.; Wang, X.; Wang, H.; Chen, V.; Ning, Y. (2023). Equipping Federated Graph Neural Networks with Structure-aware Group Fairness. IEEE International Conference on Data Mining (ICDM).. IEEE International Conference on Data Mining (ICDM)..
12. 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.
13. 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.
14. Chen, J.; Creamer, G.; Ning, Y. (2022). Forecasting Emerging Pandemics with Transfer Learning and Location-aware News Analysis. IEEE BigData. IEEE BigData.
15. Han, X.; Ning, Y. (2022). Text-enhanced Multi-Granularity Temporal Graph Learning for Event Prediction. 2022 IEEE International Conference on Data Mining (ICDM). Proceedings of the 22nd IEEE International Conference on Data Mining (pp. 171-180). IEEE.
    https://doi.org/10.1109/icdm54844.2022.00027.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. 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.
25. 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.
26. 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.
27. 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.
28. 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.
29. 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.
30. 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.
31. 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.
32. 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. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.