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 Data Mining and Planning in logistic scenarios.

Email: [email protected]; [email protected]

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.

• J. Fang, B. Zhou, H. Wang, X. Zhu, L. Wang, “Effective Online 3D Bin Packing with Lookahead Parcels Using Monte Carlo Tree Search”. KDD 2026.

• J. Fang^, L. Chen^, D. Chai^, Y. Hong, X. Xie, L. Chen, Leye Wang. “UCTB: an Urban Computing Tool Box for All-in-one Spatiotemporal Prediction Solution”. CCF Trans. Pervasive Computing and Interaction (2025).

• 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.