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Statistical Learning for Physical/Engineering Processes
Statistical Learning for Physical/Engineering Processes
domain-aware statistical learning and prediction for nonlinear dynamics (e.g., advection-diffusion, structural dynamics, etc.), reduced-order modeling, inverse modeling, and optimal experimental design.
Resilience & Environment
Resilience & Environment
remote-sensing data modeling for environmental processes (e.g., propagation of smoke and aerosols, fire spread models, etc.), wildfires and power grid resilience, stochastic degradation and maintenance, reliability, and survival analysis.
Data Science & Applied Statistics
Data Science & Applied Statistics
ensemble trees for recurrence data, gradient boosted trees for Gaussian process, structural boosting trees for edge detection, etc.
Examples of on-going and completed research projects:
Examples of on-going and completed research projects:
My research has been primarily supported by the National Science Foundation (NSF), and some on-going research projects and complete projects can be found below.
On-Going Research I: Statistical Learning for Dynamical Systems
On-Going Research II: Wildfires-Energy-Environment-Resilience
On-Going Research III: Statistical Learning for Reduced-Order Modeling
On-Going Research IV: Wafer Chip Warpage Modeling and Control
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