San Joaquin Valley's (SJV) residents face challenges of air pollutant exposure -- the basin terrain of the SJV traps air pollutants easily. At the same time, the local land use (e.g., industry, agriculture) results in a high variation of pollutant sources of PM2.5. We develop Artificial Intelligence of Things (AIoT) with both advanced sensing and learning algorithms to tackle the challenge with low-cost and scalable solutions.
Latest Papers:
Shangjie Du, Hui Wei, Dong Yoon Lee, Zhizhang Hu, Shijia Pan. 2025. Graph-Based Physics-Guided Urban PM2.5 Air Quality Imputation with Constrained Monitoring Data. ACM Transactions on Sensor Networks (TOSN).
Shangjie Du, Zhizhang Hu, and Shijia Pan. 2024. GraPhy: Graph-Based Physics-Guided Urban Air Quality Modeling for Monitoring-Constrained Regions. In Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’24), November 7–8, 2024, Hangzhou, China.
Zhizhang Hu∗, Shangjie Du∗, Yuning Chen, Xuan Zhang, Wan Du, Asa Bradman, and Shijia Pan. 2023. Poster Abstract: Enhancing Fault Resilience of Air Quality Monitoring in San Joaquin Valley: A Data Equity Analysis. In Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems (SenSys ’23), November 12–17, 2023, Istanbul, Turkiye. ACM, New York, NY, USA, 2 pages.
Gang Wang, Shijia Pan, and Susu Xu. "Decoupling the unfairness propagation chain in crowd sensing and learning systems for spatio-temporal urban monitoring." In Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp. 200-203. 2021.
Award: Best Paper Award Runner-Up BuildSys'24