期刊文章:

[1] 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.（COMMTR） 【SCI Q1，IF 14.5】

[2] 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. 【SCI Q1，IF 25.7】

[3] 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).【CCF-A，SCI Q1，IF 10.4】

[4] 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). 【CCF-A，SCI Q1，IF 10.4】

[5] 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】

[6] 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】

[7] 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】

[8] 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】

[9] 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】

[10] 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】

[11] Xu*, Y., Wang*, F., & Zhang*, T. (2024). Artificial intelligence is restructuring a new world. The Innovation, 5(6). 【SCI Q1，Web of Science综合类排名第四，IF 25.7】

[12] 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). 【CCF-A，SCI Q1，IF 10.4，ESI高被引、ESI热点】

[13] 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】

[14] 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】

[15] 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】

[16] 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】

[17] 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】

[18] 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】

[19] 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】

[20] 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】

[21] 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】

[22] 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] 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). Accepted. 【CCF-A】

[2] 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. The Thirty-ninth Annual Conference on Neural Information Processing Systems(NeurIPS). Accepted. 【CCF-A】

[3] 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). Accepted. 【CCF-A】

[4] 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). Accepted. 【CCF-A】

[5] 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】

[6] 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】

[7] 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】

[8] 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】

[9] 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】

[10]  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】

[11] 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】

[12] 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】

[13] 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】

[14] 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】

[15] 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】

[16] 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】

[17] 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】

[18] 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】

[19] 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】

[20] 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）】

[21] 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】

[22] 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】

[23] 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】

[24] 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】

[25] 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】

专利：

[1] 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：2025年10月

[2] 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

[3] 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

[4] 一种用于目标跟踪的多假设树虚拟化管理方法，授权号：CN 112181667 B，授权时间：2023年8月

[5] 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

[6] 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

[7] 一种时序知识图谱推理模型构建方法、推理方法，受理号：CN202411947462.4

[8] 一种用于匿名时空轨迹识别的模型，受理号：CN202411433967.9

[9] 一种多源域融合的时空轨迹基础模型构建方法及其应用，受理号：CN202511429632.4

[10] 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

[11] 一种用于机动目标的轨迹观测数据去噪模型的训练方法，受理号：CN202310226232.8

[12] 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，受理号：CN202311156428.0

[13] 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

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

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

[16] 一种面向非完整多元时间序列预测的学习方法，受理号：CN202410834572.3

[17] 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

[18] 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

[19] 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

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

[21] 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

[22] 一种基于隐空间条件约束生成模型的轨迹恢复方法，受理号：CN202511483608.9

[23] 一种用于空基平台观测的航迹关联目标起批率评估与计算方法及系统，受理号：CN202511688278.7

[24] 基于预训练增强的多元时间序列预测方法及系统，受理号：CN202211144325.8

[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

2024年度中央和国家机关“四好党员”

2025年度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年度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年度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