Publications

The publications below are listed in chronological order. You can also find my articles on my Google Scholar profile.

In the year of 2024

1. [ICLR’24] AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction (Acceptance Ratio=31%, CORE Rank A*)
   K. Hettige, J. Ji, S. Xiang, C. Long, G. Cong, J. Wang
   International Conference on Learning Representations
   [pdf][code][bib]

In the year of 2023

1. [Interspeech’23] Enhancing New Intent Discovery via Robust Neighbor-based Contrastive Learning (CCF C, Core Rank A)
   Z. Wu, X. Yu, M. Chen, L. Wu, J. Ji, and Z. Li
   INTERSPEECH Conference
   [pdf][bib]

2. [Weekly’23] High-Resolution Data on Human Behavior for Effective COVID-19 Policy-Making — Wuhan City, Hubei Province, China, January 1–February 29, 2020 (SCI)
   J. Wang, H. Shi, J. Ji, X. Lin, H. Tian
   China CDC Weekly
   [pdf][bib][web]

3. [AAAI’23] Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction (Acceptance Ratio=19.6%, CCF A)
   J. Ji, J. Wang, C. Huang, J. Wu, B. Xu, Z. Wu, J. Zhang and Y. Zheng
   AAAI Conference on Artificial Intelligence
   [pdf][code][bib][media][web]

In the year of 2022

1. [TKDE’22] Traffic Flow Prediction Based on Spatiotemporal Potential Energy Fields (IF=9.235, CCF A)
   J. Wang, J. Ji, Z. Jiang, L. Sun
   IEEE Transactions on Knowledge and Data Engineering
   [pdf][bib][web]

2. [ECML/PKDD’22] DialCSP: A Two-stage Attention-based Model for Customer Satisfaction Prediction in E-commerce Customer Service (Acceptance Ratio=26.0%, CCF B)
   Z. Wu, L. Wu, S. Song, J. Ji, B. Zou, Z. Li, and X. He
   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
   [pdf][bib][web]

3. [KDD’22] Precision CityShield Against Hazardous Chemicals Threats via Location Mining and Self-Supervised Learning (Acceptance Ratio=25.9%, CCF A)
   J. Ji, J. Wang, J. Wu, B. Han, J. Zhang, and Y. Zheng
   ACM International Conference on Knowledge Discovery and Data Mining
   [pdf][bib][web]

4. [AAAI’22] STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction (Acceptance Ratio=15.0%, CCF A)
   J. Ji, J. Wang, Z. Jiang, J. Jiang, and H. Zhang
   AAAI Conference on Artificial Intelligence
   [pdf][code][bib][media][web]

Before the year of 2022

1. [ICDM’20] Interpretable Spatiotemporal Deep Learning Model for Traffic Flow Prediction Based on Potential Energy Fields (Acceptance Ratio=9.9%, CCF B)
   J. Ji, J. Wang, Z. Jiang, J. Ma, and H. Zhang
   IEEE International Conference on Data Mining
   [pdf][bib][web]