Stochastic Modeling & Simulation of Natural Hazards
We develop stochastic models and simulations to characterize natural hazards — including wildfires, hurricanes, tornadoes, and earthquakes — capturing their inherent uncertainty and variability. Grounded in probability and statistics, this work increasingly incorporates machine learning and deep learning-based methods to produce more realistic hazard characterizations that support downstream risk and vulnerability assessments.
Related Projects
Coming soon.
Representative Publications
- Jonnalagadda, V.*, Lee, J.Y., and Sanad, A-A.* (2026). “Cluster-based active-learning scenario reduction framework for probabilistic wildfire risk assessment.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 12(2), 04026016.
- Lee, J.Y. and Ellingwood, B.R. (2017). “A decision model for intergenerational life-cycle risk assessment of civil infrastructure exposed to hurricanes under climate change.” Reliability Engineering and System Safety, 159: 100-107.
