Yue Ning

• PhD (2018) Virginia Tech (Computer Science)

• 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

• 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

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

Conference Proceeding

1. 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.
2. 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.
3. 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.
4. Deng, S.; de Rijke , M.; Ning, Y. (2024). Advances in Human Event Modeling: From Graph Neural Networks to Language Models. ACM SIGKDD.
5. 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)..
6. 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.
7. 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.
8. Chen, J.; Creamer, G.; Ning, Y. (2022). Forecasting Emerging Pandemics with Transfer Learning and Location-aware News Analysis. IEEE BigData. IEEE BigData.
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. 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.
25. 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.
26. 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.