Research Interests

1. Scientific Machine learning
   • Data driven models, System identification, Neural and deep learning for physics-informed applications.
2. Numerical methods for forward and inverse problems
   • Ensemble learning, Bayesian inference, and Numerical optimization methods with applied for model order reduction, PDE-Constrained optimization and inverse problems
3. Quantitative Social science research
   • Exploratory and explanatory data analysis for chronic disease prevention intervention
   • Quantitative social research at the intersection of online learning and Gerontology
4. Time-stepping methods for PDEs
   • High-order time discretizations for multi-physics systems. Implicit-Explicit, variable time-stepping and error control strategies. Parallel and Jacobian-free methods Advanced methods for fluid simulations, DAEs and stiff problems

Scientific Machine Learning

Multirate Methods for ODEs

Chronic Disease Prevention