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 29^th 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 29^th 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 29^th 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 29^th 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.