Biography

Currently, I am a PhD student supervised by Prof. Leye Wang at School of Computer Science, Peking University. My research interests include spatio-temporal prediction, domain-specific time series foundation model with special focus on spatial effeccts and context modeling.

Email: fangjiangyi2001@gmail.com; fangjiangyi@stu.pku.edu.cn

Google Scholar

Highlight Research Project

Urban Computing Tool Box (UCTB) is a library providing spatio-temporal paper list, urban datasets, spatio-temporal prediction models, visualization tools, and survey of Timeseries Foundation Models for various urban computing tasks, such as traffic prediction, crowd flow prediction, ridesharing demand prediction, etc.

We highly recommend you use UCTB and looking forward to your feedback.

Education Experience

• 2019~2023 School of Artificial Intelligence and Automation, Huazhong University of Science and Technology.
• 2023~now School of Computer Science, Peking University.

Publications

Name with ^ means equally contribution.

• Y. Xiang, J. Fang, C. Li, H. Yuan, Y. Song, J. Chen, “Effective AOI-level Parcel Volume Prediction: When Lookahead Parcels Matter”. KDD 2025.

• J. Fang^, L. Chen^, D. Chai^, Y. Hong, X. Xie, L. Chen, L. Wang, “UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services”. SSE 2024.

• L. Chen, J. Fang, T. Liu, S. Cao, L. Wang, “A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units”. ICDE 2024.

• L. Chen, J. Fang, Z. Yu, Y. Tong, S. Cao, L. Wang, “A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management”. KDD 2023.