My name is highlighted in bold,  * denotes the co-first authorship.

Topics: ◼Deep Learning and Data Mining ◼Intelligent Air Transportation Systems

Conference papers

◼[C13] (NeurIPS 2025) Y. Jiang, W. Yu, G. Lee, D. Song, K. Shin, W. Cheng and H. Chen, Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop, The 39th Annual Conference on Neural Information Processing Systems [pdf]

◼[C12] (NeurIPS 2025) K. Ning, Z. Pan, Y. Liu, Y. Jiang, J. Zhang, K. Rasul, A. Schneider, Y. Nevmyvaka and D. Song, TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster, The 39th Annual Conference on Neural Information Processing Systems [pdf]

◼[C11] (KDD 2025) Y. Jiang, K. Ning, Z. Pan, X. Shen,  J.  Ni, W. Yu, A. Schneider, H. Chen, Y. Nevmyvaka, and D. Song, Multi-modal Time Series Analysis: A Tutorial and Survey, 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining [pdf]

◼[C10] (ICLR 2025) X. Zhang, D. Song, Y. Jiang, Y. Chen, and D. Tao, Learning System Dynamics without Forgetting, 13th International Conference on Learning Representations [pdf] 

◼[C9] (IJCAI 2024) Y. Jiang, Z. Pan, X. Zhang, S. Garg, A. Schneider, Y. Nevmyvaka, and D. Song, Empowering Time Series Analysis with Large Language Models: A Survey, 2024 International Joint Conference on Artificial Intelligence [pdf](collaborated with Morgan Stanley Machine Learning Research Department)

◼[C8] (ICML 2024) Z. Pan, Y. Jiang, S. Garg, A. Schneider, Y. Nevmyvaka, and D. Song, S²IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting, 41st International Conference on Machine Learning [pdf] (collaborated with Morgan Stanley Machine Learning Research Department)

◼[C7] (KDD 2024) Y. Liang, H. Wen, Y. Nie, Y. Jiang, M. Jin, D. Song, S. Pan, and Q. Wen, Foundation Models for Time Series Analysis: A Tutorial and Survey, 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining [pdf] 

◼[C6] (KDD 2023) Y. Jiang, W. Yu, D. Song, L. Wang, W. Cheng and H. Chen, FedSkill: Privacy Preserved Interpretable Skill Learning via Imitation, 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining [pdf] (collaborated with NEC Labs DSSS Department)

◼[C5] (CISS 2023) Y. Jiang, W. Yu, D. Song, W. Cheng and H. Chen, Interpretable Skill Learning for Dynamic Treatment Regimes through Imitation, 57th Annual Conference on Information Sciences and Systems [pdf] (collaborated with NEC Labs DSSS Department)

◼[C4] (IPCCC 2021) K. Zhang*, Y. Jiang*, L. Seversky, C. Xu, et al., "Federated Variational Learning for Anomaly Detection in Multivariate Time Series," IEEE 40th International Performance Computing and Communications Conference [pdf] (Air Force Research Laboratory Summer Research Program) 

◼◼[C3] (ICASSP 2021) C. Xu, F. He, B. Chen, Y. Jiang, et al., "Adaptive RF Fingerprint Decomposition in Micro UAV Detection based on Machine Learning", 2021 IEEE International Conference on Acoustics, Speech and Signal Processing  [pdf]

◼◼[C2] (IPCCC 2020) K. Zhang, Y. Jiang, et al., "Spatio-Temporal Data Mining for Aviation Delay Prediction," IEEE 39th International Performance Computing and Communications Conference [pdf] [arXiv] 

◼◼[C1] (CBDCOM 2020) Jiang, Y. Liu, et al., "Applying Machine Learning to Aviation Big Data for Flight Delay Prediction", 2020 IEEE International Conference on Cloud and Big Data Computing [pdf] 

Journal papers

◼◼[J3] Y. Jiang, S. Niu, K. Zhang, B. Chen, et al., "Spatial-Temporal Graph Data Mining for IoT-enabled Air Mobility Prediction," IEEE Internet of Things Journal. [pdf]

◼[J2] S. Niu, Y. Jiang, B. Chen, et al., "Cross-Modality Transfer Learning for Image-Text Information Management", ACM Transactions on Management Information Systems. [pdf]

◼[J1] C. Xu, K. Zhang, Y. Jiang, S. Niu, T. Yang, et al., "Communication Aware UAV Swarm Surveillance Based on Hierarchical Architecture," MDPI Drones. (Editor’s Choice Articles) [pdf]

Tutorials

◼[T5] Multi-Modal Time Series Analysis: Data, Methods, and Applications, 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025).[website]

◼[T4] Foundation Models for Time Series Analysis: A Tutorial, 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025). [website]

◼[T3] Foundation Models for Time Series Analysis: A Tutorial, 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024).  [website]

◼[T2] Continual Graph Learning, ACM Web Conference 2023 (WWW 2023). [website]

◼[T1] Continual Graph Learning, 23rd SIAM International Conference on Data Mining (SDM 2023). [website]