Research
Embedded Files
Spatio-Temporal Data Analytics
My current research focuses on spatial-temporal data analytics, with an emphasis on developing interpretable statistical models that support reliable and efficient inference for improved decision-making. These models are designed to integrate context-specific information (e.g., healthcare domain knowledge and system structure information) along with the underlying mechanisms driving event occurrences (e.g., triggering mechanism and inhibiting mechanism). These efforts are expected to enhance the predictive capability of models and facilitate more informed decision-making processes within complex systems.
Methodologies: Stochastic point process, statistical learning, optimization method, high-dimensional statistics, survival analysis, physics-based simulation
Applications: Artificial intelligence (AI) systems, healthcare informatics, transportation systems, social media networks, supply chain networks, and additive manufacturing.
Inter-disciplinary Applications
Complex systems reliability modeling
Zheng, S., Clark, J.M., Salboukh, F., Silva, P., da Mata, K., Pan, F., Min, J., Lian, J., King, C.B., Fiondella, L. and Liu, J., 2025. "DR-AIR: A data repository bridging the research gap in AI reliabilit". Accepted by the Journal of Quality Engineering.
Pan, F., et al, (2024). “Reliability Modeling for Perception Systems in Autonomous Vehicles: A Recursive Event-Triggering Point Process”. Published in the Journal of Transportation Research Part C.
2023 INFORMS Conference on Quality, Statistics, and Reliability Best Paper Award to Finalist
Pan, F.,et al, (2022). "Quantifying Error Propagation in Multi-Stage Perception System of Autonomous Vehicles via Physics-Based Simulation". Publised in the 2022 Winter Simulation Conference (WSC).
Healthcare Informatics
Pan, F., Zhou, Y., Valencia, C., Kong, N., Ott, C., Jalalie, M., and Liu, J. (2024). "Modeling Opioid Overdose Recurrence with a Covariate-Adjusted Mutually-Triggering Point Process (CAMTPP)". Publised in the Journal of PLOS Computational Biology.
2024 Arizona Data Science Day Best Poster
Yang, H., Pan, F. (co-first author), Tong, D., Brown, H.E. and Liu, J., 2024. "Measurement Error–tolerant Poisson Regression for Valley Fever Incidence Prediction." Publised in the Journal of IISE Transactions on Healthcare Systems Engineering.
Featured in the IISE magazine.
Large-scale traffic demand prediction
Ehsani, S., Pan, F., Hu, Q. and Liu, J., 2025. "BiDepth Multimodal Neural Network: Bidirectional Depth Deep Learning Arcitecture for Spatial-Temporal Prediction". Under review in the Journal of Expert Systems With Applications.
Page updated
Report abuse