期刊文章:

[1] Li, R., Tang, D., Wang, F., Zhu, X., & Cao, X. (2026). SpectraBayes: Exploring Traffic Series Reconstruction in Frequency Domain for Anomaly Detection. IEEE Transactions on Intelligent Transportation Systems.（IEEE TITS） 【SCI Q1，IF 8.4】

[2] Shao, Z., Wang*, F., Zhang, Z., Fang, Y., Jin, G., & Xu, Y. (2025). Hutformer: Hierarchical u-net transformer for long-term traffic forecasting. Communications in Transportation Research, 5 (2025): 100218.（COMMTR） 【SCI Q1，IF 14.5】

[3] Huang, J., Xu, Y., Wang, Q., Wang, Q. C., Liang, X., Wang, F., ... & Fei, A. (2025). Foundation models and intelligent decision-making: Progress, challenges, and perspectives. The Innovation, 6(6). 【SCI Q1，IF 25.7】

[4] Yu, C., Wang*, F., Shao, Z., Qian, T., Zhang, Z., Wei, W., ... & Xu, Y. (2025). GinAR+: A Robust End-To-End Framework for Multivariate Time Series Forecasting with Missing Values. IEEE Transactions on Knowledge and Data Engineering(TKDE), 37(8), pp. 4635-4648.【CCF-A，SCI Q1，IF 10.4】

[5] Guan, Z., Zhang, F., Zhang, Z., Zhuang, F., Wang, F., An, Z., & Xu, Y. (2025). AdaE: Knowledge Graph Embedding with Adaptive Embedding Sizes. IEEE Transactions on Knowledge and Data Engineering (TKDE), 37(8), pp. 4432-4445. 【CCF-A，SCI Q1，IF 10.4】

[6] Chen, S., Liao, Y., Wang, F., Wang, G., Wang, L., Wang, Y., & Zhu, X. (2025). Toward the robustness of autonomous vehicles in the AI era. The Innovation, 6(3). 【SCI Q1，IF 25.7】

[7] Zhang, Y., Lin, Y., Zheng, G., Liu, Y., Sukiennik, N., Xu, F., Yong, X., Feng, L., Qi, W., Yuan, L., Li, T., Dong, F., Wang, F., ... & Guo, H. (2025). MetaCity: Data-driven sustainable development of complex cities. The Innovation, 6(2). 【SCI Q1，IF 25.7】

[8] Shao, Z., Qian, T., Sun, T., Wang*, F., & Xu, Y. (2025). Spatial-temporal large models: A super hub linking multiple scientific areas with artificial intelligence. The Innovation, 6(2). 【SCI Q1，IF 25.7】

[9] Li, Y., Sun, T., Shao, Z., Zhen, Y., Xu, Y., & Wang*, F. (2025). Trajectory-user linking via multi-scale graph attention network. Pattern Recognition, 158, 110978. 【CCF-B，SCI Q1，IF 7.6】

[10] Wu, Y., Zhang, Z., Wang, F., Xu, Y., & Huang, J. (2025). Toward more economical large-scale foundation models: No longer a game for the few. The Innovation, 6(4). 【SCI Q1，IF 25.7】

[11] Yu, C., Wang*, F., Wang, Y., Shao, Z., Sun, T., Yao, D., & Xu, Y. (2025). Mgsfformer: A multi-granularity spatiotemporal fusion transformer for air quality prediction. Information Fusion, 113, 102607. 【SCI Q1，IF 14.7, 信息融合领域顶刊】

[12] Xu*, Y., Wang*, F., & Zhang*, T. (2024). Artificial intelligence is restructuring a new world. The Innovation, 5(6). 【SCI Q1， IF 25.7】

[13] Shao, Z., Wang*, F., Xu, Y., Wei, W., Yu, C., Zhang, Z., ... & Cheng, X. (2024). Exploring progress in multivariate time series forecasting: Comprehensive benchmarking and heterogeneity analysis. IEEE Transactions on Knowledge and Data Engineering(TKDE), 37(1), pp. 291-305. 【CCF-A，SCI Q1，IF 10.4，ESI高被引、ESI热点】

[14] Yinhan, Wang., Jiang, Wang., Shaoming, He., Fei, Wang., & Qi, Wang. (2024). Swarm intention identification via dynamic distribution probability image. Chinese Journal of Aeronautics, 37(10), 380-392.【SCI Q1，IF 5.7】

[15] Zhao, T., Wang, S., Ouyang, C., Chen, M., Liu, C., Zhang, J., Long, Y., Wang, F., ... & Wang, L. (2024). Artificial intelligence for geoscience: Progress, challenges, and perspectives. The Innovation, 5(5). 【SCI Q1，IF 25.7】

[16] Yu, Y., Wang, Z., Wei, W., Zhang, R., Mao, X. L., Feng, S., Wang, F., ... & Jiang, S. (2024). Exploiting global contextual information for document-level named entity recognition. Knowledge-Based Systems, 284, 111266. 【SCI Q1，IF 7.6】

[17] Xu, Y., Wang*, F., An, Z., Wang, Q., & Zhang, Z. (2023). Artificial intelligence for science—bridging data to wisdom. The Innovation, 4(6). 【SCI Q1，IF 25.7】

[18] Wang, P., Hu, Q., Xie, W., Wu, L., Wang, F., & Mei, Q. (2023). Big data–driven carbon emission traceability list and characteristics of ships in maritime transportation—a case study of Tianjin Port. Environmental Science and Pollution Research, 30(27), 71103-71119. 【SCI Q1，IF 5.8】

[19] Wang, Q., Li, T., Xu, Y., Wang, F., Diao, B., Zheng, L., & Huang, J. (2023). How to prevent malicious use of intelligent unmanned swarms?. The Innovation, 4(2). 【SCI Q1，IF 25.7】

[20] Wang, F., Yao, D., Li, Y., Sun, T., & Zhang, Z. (2023). AI-enhanced spatial-temporal data-mining technology: New chance for next-generation urban computing. The Innovation, 4(2). 【SCI Q1，IF 25.7】

[21] Wang, Q., Dong, C., Jian, S., Du, D., Lu, Z., Qi, Y., Han, D., Ma, X, Wang, F., & Liu, Y. (2023). HANDOM: Heterogeneous attention network model for malicious domain detection. Computers & Security, 125, 103059. 【CCF-B ，SCI Q1，IF  5.4】

[22] Shao, Z., Xu, Y., Wei, W., Wang*, F., Zhang, Z., & Zhu, F. (2022). Heterogeneous graph neural network with multi-view representation learning. IEEE Transactions on Knowledge and Data Engineering(TKDE), 35(11), 11476-11488. 【CCF-A ，SCI Q1，IF  10.5】

[23] Shen, M., Lu, H., Wang, F., Liu, H., & Zhu, L. (2022). Secure and efficient blockchain-assisted authentication for edge-integrated Internet-of-Vehicles. IEEE Transactions on Vehicular Technology, 71(11), 12250-12263. 【SCI Q1，IF 7.1】

会议文章： 

[1] Fu, Y., Shao, Z., Yu, C., Li, Y., Xu, Y., Cheng, X., Wang*, F. (2026). Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis. International Conference on Machine Learning (ICML). Accepted.【CCF-A】

[2] Zhong, S., Liu, Y., Cui, Z., Shao, Z., Wang, F., Wen, Q., & Liang, Y. (2026). DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting. International Conference on Machine Learning (ICML). Accepted.【CCF-A】

[3] Liu, Y., Shao, Z., Chen, X., Chen, H., Wang, F., & Wu, Y. (2026). PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting. International Conference on Machine Learning (ICML). Accepted.【CCF-A】

[4] Liu, W., Li, Z., Bai, L., Zhang, C., Zhang, F., Chen, Z., Li, W., Zuo, Y., Wang, F., ... & Cheng, X. (2026). Towards Knowledgeable Deep Research: Framework and Benchmark. ACM Special Interest Group on Information Retrieval (SIGIR). Accpeted.【CCF-A】

[5] Huang, L., An, Z., Yang, C., Diao, B., Wang, F., Zeng, Y., ... & Xu, Y. (2026). PrePrompt: Predictive prompting for class incremental learning. ACM SIGKDD conference on knowledge discovery and data mining (KDD). Accpeted.【CCF-A】

[6] Chen, Z., Wang*, F., Li*, Z., Zhang, Z., Ding, W., Yang, C., Xu, Y. and Jin, X. (2026). Incentivizing Agentic Reasoning Capability with Outcome Supervision for KBQA. the International World Wide Web Conference (WWW). Accepted. 【CCF-A】

[7] Li, Y., Shao, Z., Chen, Y., Fu, Y., Sun, T., Xu, Y. & Wang*, F. (2025). APT: Affine Prototype-Timestamp For Time Series Forecasting Under Distribution Shift. The Fortieth AAAI Conference on Artificial Intelligence(AAAI).(pp. 15180-15188). 【CCF-A】

[8] Qian, T., Li, J., Chen, Y., Cong, G., Shao, Z., Zhang, J., Sun, T., Wang*, F. & Xu, Y. (2025).  SMARTraj^2: A Stable Multi-City Adaptive Method for Multi-View Spatio-Temporal Trajectory Representation Learning. In Proceedings of The Thirty-ninth Annual Conference on Neural Information Processing Systems(NeurIPS). (pp. 163673-163699). 【CCF-A】

[9] Fu, Y., Wang*, F., Shao, Z., Diao, B., Wu, L., … & Xu, Y. (2025).  On the Integration of Spatial-Temporal Knowledge: A Lightweight Approach to Atmospheric Time Series Forecasting. The Thirty-ninth Annual Conference on Neural Information Processing Systems(NeurIPS). (pp. 82509-82538). 【CCF-A】

[10] Fu, Y., Shao, Z., Yu, C., Li, Y., An, Z., Wang, Q., Xu, Y. & Wang*, F. (2025). Selective Learning for Deep Time Series Forecasting. The Thirty-ninth Annual Conference on Neural Information Processing Systems(NeurIPS). (pp. 98084-98115). 【CCF-A】

[11] Li, Y., Shao, Z., Chen, Y., … Wang*, F. & Xu, Y. (2025). Sta-gann: A valid and generalizable spatio-temporal kriging approach. Proceedings of the 34th ACM International Conference on Information and Knowledge Management(CIKM). 1726-1736. 【CCF-B】

[12] Yu, C., Wang*, F., Yang, C., Shao, Z., Sun, T., ... & Xu, Y. (2025). Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates. In Proceedings of the 31st ACM SIGKDD conference on knowledge discovery and data mining (KDD). 【CCF-A】

[13] Shao, Z., Li, Y., Wang*, F., Yu, C., Fu, Y., Qian, T., ... & Cheng, X. (2025). BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models. In Proceedings of the 31st ACM SIGKDD conference on knowledge discovery and data mining (KDD). 【CCF-A】

[14] Zhou, W., Wei, W., Cao, G., & Wang, F. (2025, April). Editing Memories Through Few Targeted Neurons. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 24, pp. 26111-26119).【CCF-A】

[15] Feng, W., Qin, H., Yang, C., An, Z., Huang, L., Diao, B., Wang, F., ... & Magno, M. (2025, April). Mpq-dm: Mixed precision quantization for extremely low bit diffusion models. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 16, pp. 16595-16603). 【CCF-A】

[16]  Qian, T. W., Wang, Y., Xu, Y. J., Zhang, Z., Wu, L., Qiu, Q., & Wang*, F. (2025). A Model-Agnostic Hierarchical Framework Towards Trajectory Prediction. Journal of Computer Science and Technology, 40(2), 322-339. 【CCF-B】

[17] Feng, W., Yang, C., An, Z., Huang, L., Diao, B., Wang, F., & Xu, Y. (2024, October). Relational diffusion distillation for efficient image generation. In Proceedings of the 32nd ACM international conference on multimedia (ACM MM) (pp. 205-213). 【CCF-A】

[18] Yu, C., Wang*, F., Shao, Z., Qian, T., Zhang, Z., Wei, W., & Xu, Y. (2024, August). Ginar: An end-to-end multivariate time series forecasting model suitable for variable missing. In Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining(KDD) (pp. 3989-4000). 【CCF-A】

[19] Qian, T., Chen, Y., Cong, G., Xu, Y., & Wang*, F. (2024, May). AdapTraj: A multi-source domain generalization framework for multi-agent trajectory prediction. In 2024 IEEE 40th International Conference on Data Engineering (ICDE) (pp. 5048-5060). IEEE. 【CCF-A】

[20] Li, Y., Shao, Z., Xu, Y., Qiu, Q., Cao, Z., & Wang*, F. (2024, April). Dynamic frequency domain graph convolutional network for traffic forecasting. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5245-5249). IEEE. 【CCF-B】

[21] Chen, Z., Zhang, Z., Li, Z., Wang*, F., Zeng, Y., Jin, X., & Xu, Y. (2024). Self-improvement programming for temporal knowledge graph question answering. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). 【CCF-B】

[22] Wang, Y., Shao, Z., Sun, T., Yu, C., Xu, Y., & Wang, F*. (2023, October). Clustering-property matters: A cluster-aware network for large scale multivariate time series forecasting. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management(CIKM)(pp. 4340-4344). 【CCF-B】

[23] Yu, C.,Wang*, F., Shao, Z., Sun, T., Wu, L., & Xu, Y. (2023, October). Dsformer: A double sampling transformer for multivariate time series long-term prediction. In Proceedings of the 32nd ACM international conference on information and knowledge management (pp. 3062-3072). 【CCF-B，入选最有影响力论文榜单】

[24] Zhang, Z., Guan, Z., Zhang, F., Zhuang, F., An, Z., Wang, F., & Xu, Y. (2023, July). Weighted knowledge graph embedding. In Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval(SIGIR) (pp. 867-877). 【CCF-B】

[25] Liang, Y., Shao, Z., Wang*, F., Zhang, Z., Sun, T., & Xu, Y. (2022, November). Basicts: An open source fair multivariate time series prediction benchmark. In International symposium on benchmarking, measuring and optimization(Bench) (pp. 87-101). Cham: Springer International Publishing. 【国际测试基准与标准大会】

[26] Shao, Z., Zhang, Z., Wang*, F., Wei, W., & Xu, Y. (2022, October). Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting. In Proceedings of the 31st ACM international conference on information & knowledge management(CIKM) (pp. 4454-4458). 【CCF-B，入选最有影响力论文榜单（CIKM 22 第1）】

[27] Qian, T., Xu, Y., Zhang, Z., & Wang*, F. (2022, October). Trajectory prediction from hierarchical perspective. In Proceedings of the 30th ACM International Conference on Multimedia (pp. 6822-6830). 【CCF-A】

[28] Shao, Z., Zhang, Z., Wang, F.*, & Xu, Y. (2022, August). Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining(KDD) (pp. 1567-1577). 【CCF-A，入选最有影响力论文榜单】

[29] Shao, Z., Zhang, Z., Wei, W., Wang*, F., Xu, Y., Cao, X., & Jensen, C. S. (2022). Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. Proceedings of the VLDB Endowment, 15(11), 2733-2746. 【CCF-A，ESI高被引】

[30] Sun, T., Wang*, F., Zhang, Z., Wu, L., & Xu, Y. (2022, April). Human mobility identification by deep behavior relevant location representation. In International Conference on Database Systems for Advanced Applications (DASFAA) (pp. 439-454). Cham: Springer International Publishing. 【CCF-B，DASFAA 2022最佳学生论文奖】

[31] Liang, S., Wei, W., Mao, X. L., Wang, F., & He, Z. (2022). BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis. In 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 (pp. 1835-1848). Association for Computational Linguistics (ACL). 【CCF-A】

授权受理专利：

[1] 一种轻量化的大尺度多元时间序列预测模型及其训练方法，授权号：CN 117113206 B，授权时间：2026年4月

[2] 一种用于机动目标的轨迹观测数据去噪模型的训练方法，授权号：CN 116432024 B，授权时间：2026年2月

[3] 一种面向非完整多元时间序列预测的学习方法，授权号： CN 119129768 B，授权时间：2026年2月

[4] 一种时序知识图谱推理模型构建方法、推理方法，授权号：CN 119849630 B，授权时间：2025年11月

[5] 一种用于匿名时空轨迹识别的模型，授权号：CN 119494967 B，授权时间：2025年11月

[6] 一种知识问答模型构建方法、知识问答系统及推理方法，授权号：CN 119829722 B，授权时间：2025年10月

[7] 一种用于多元时间序列分析的外插模型及其训练方法，授权号：CN 119669663 B，授权时间：2025年10月

[8] 基于DSP加速计算板卡的无人平台目标检测识别方法与系统，授权号：CN 112132235 B，授权时间：2023年8月

[9] 一种基于嵌入-混合的轨迹数据增强及轨迹识别方法，授权号：CN 112949628 B，授权时间：2023年4月

[10] 一种大规模移动对象的轨迹快速预测方法、介质和设备，授权号：CN 111291280 B，授权时间：2023年4月

[11] 一种基于多尺度融合的多元时间序列预测模型构建方法，受理号：CN202411430489.6

[12] 一种面向数据缺失的多元时间序列数据预测模型训练方法，受理号：CN202311547462.0

[13] 一种多目标跟踪的容量极限估计方法及系统，受理号：CN202410176786.6

[14] 一种基于大数据的海上航线规划系统，受理号：CN202410297830.9

[15] 一种基于多元时序预测模型的时间序列预测方法及其系统，受理号：CN202511370450.4

[16] 一种交通流预测系统的构建方法、交通流预测方法，受理号：CN202511510886.9

[17] 一种海洋涡旋预测系统的构建方法、海洋涡旋预测方法，受理号：CN202511463619.0

[18] 一种基于海表数据估计三维温盐结构的方法，受理号：CN202511351449.7

[19] 一种海洋涡旋时间序列预测系统构建方法与预测方法，受理号：CN202511549056.7

[20] 一种多视角时空互补的轨迹预测方法，受理号：CN02511713805.5

[1]国家自然科学基金面上项目：具有尺度适应性的多元时间序列深度预测技术与应用（2024-2028），项目负责人

[2]国家自然科学基金青年项目：车载自组织网络隐私安全保护（2020-2022），项目负责人

[3]中国科学院“西部之光-西部交叉团队”实验室专项（2025），东部地区项目负责人

2025 中央和国家机关“四好党员”

2025 中国指控学会技术发明奖一等奖（第2完成人）

2025 The Innovation 《创新》期刊最佳论文奖、最佳审稿人奖

2024 中国科学院计算所奖教金

2024 The Innovation 《创新》期刊最佳论文奖

2022 中国指控学会科技进步奖一等奖（第9完成人）

2021 中国科学院计算所“优秀党员”荣誉称号

2021 中国科学院计算所“新百星”荣誉称号

2020 中国指控学会科技进步奖二等奖（第3完成人）

2024/2023/2018 中国科学院计算所“优秀研究人员”荣誉称号

2016 中国科学院计算所“优秀党员”荣誉称号