个人简介：王旭，男，1997年生，博士，现任中国科学技术大学苏州高等研究院特任副研究员。2017年于东北大学信息学院获得学士学位，2023年于中国科学技术大学大数据学院获得博士学位。主要从事时空数据挖掘、时间序列分析与AI+化学等方面的研究。

电子邮箱：wx309@ustc.edu.cn

个人主页：http://home.ustc.edu.cn/~wx309/

主要研究方向：时空数据挖掘、时间序列分析与AI+化学

学术论文：

[1]    Wang, X., Wang, P., Wang, B., Zhang, Y., Zhou, Z., Bai, L., & Wang, Y. (2023). Latent Gaussian Processes based Graph Learning for Urban Traffic Prediction. IEEE Transactions on Vehicular Technology.

[2]    Wang, X., Zhang, H., Wang, P., Zhang, Y., Wang, B., Zhou, Z., & Wang, Y. (2023, August). An Observed Value Consistent Diffusion Model for Imputing Missing Values in Multivariate Time Series. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2409-2418).

[3]    Wang, X., Chen, L., Zhang, H., Wang, P., Zhou, Z., & Wang, Y. (2023, February). A Multi-graph Fusion Based Spatiotemporal Dynamic Learning Framework. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (pp. 294-302).

[4]    Zhou, Z., Shi, J., Zhang, H., Chen, Q., Wang, X.*, Chen, H., & Wang, Y. (2024). CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining 2024.

[5]    Wang, B., Zhang, Y., Wang, X., Wang, P., Zhou, Z., Bai, L., & Wang, Y. (2023, August). Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic Prediction. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2223-2232).

[6]    Yang, K., Zhou, Z., Sun, W., Wang, P., Wang, X., & Wang, Y. (2023, August). EXTRACT and REFINE: Finding a Support Subgraph Set for Graph Representation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2953-2964).

[7]    Zhou, Z., Huang, Q., Yang, K., Wang, K., Wang, X., Zhang, Y., ... & Wang, Y. (2023). Maintaining the Status Quo: Capturing Invariant Relations for OOD Spatiotemporal Learning. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 3603–3614).

[8]    Wang, B., Zhang, Y., Shi, J., Wang, P., Wang, X., Bai, L., & Wang, Y. (2023). Knowledge Expansion and Consolidation for Continual Traffic Prediction With Expanding Graphs. IEEE Transactions on Intelligent Transportation Systems.

[9]    Wang, K., Liang, Y., Wang, P., Wang, X., Gu, P., Fang, J., & Wang, Y. (2022, September). Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective. In The Eleventh International Conference on Learning Representations.

[10] Wang, P., Wang, X., Wang, B., Zhang, Y., Bai, L., & Wang, Y. (2023, April). Long-Tailed Time Series Classification via Feature Space Rebalancing. In International Conference on Database Systems for Advanced Applications (pp. 151-166). Cham: Springer Nature Switzerland.

[11] Zhang, Y., Lu, W., Wang, X., Wang, P., & Wang, Y. (2023, June). Pondering About Task Spatial Misalignment: Classification-Localization Equilibrated Object Detection. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.

[12] Wang, B., Zhang, Y., Wang, P., Wang, X., Bai, L., & Wang, Y. (2023, April). A Knowledge-Driven Memory System for Traffic Flow Prediction. In International Conference on Database Systems for Advanced Applications (pp. 192-207). Cham: Springer Nature Switzerland.

[13] 周正阳，刘浩，王琨，王鹏焜，王旭，汪炀*， 基于教师-学生时空半监督网络的城市事件预测方法. 电子学报 2023.

[14] Wang, K., Zhou, Z., Wang, X., Wang, P., Fang, Q., & Wang, Y. (2022). A2DJP: A two graph-based component fused learning framework for urban anomaly distribution and duration joint-prediction. IEEE Transactions on Knowledge and Data Engineering.

[15] Wang, P., Zhu, C., Wang, X., Zhou, Z., Wang, G., & Wang, Y. (2022). Inferring intersection traffic patterns with sparse video surveillance information: An st-gan method. IEEE Transactions on Vehicular Technology, 71(9), 9840-9852.

[16] Wang, P., Wang, X., Wang, B., Zhang, Y., Bai, L., & Wang, Y. (2022, November). Countering Modal Redundancy and Heterogeneity: A Self-Correcting Multimodal Fusion. In 2022 IEEE International Conference on Data Mining (ICDM) (pp. 518-527). IEEE.

[17] Wang, P., Ge, C., Zhou, Z., Wang, X., Li, Y., & Wang, Y. (2021). Joint gated co-attention based multi-modal networks for subregion house price prediction. IEEE Transactions on Knowledge and Data Engineering.