- Optimization under uncertainty (OUU)
- Uncertainty quantification (UQ)
- Surrogate-based design optimization
- Use-inspired research
- Multifidelity methods
- Risk analysis
- Monte Carlo methods
- Coupled multidisciplinary systems
- Scientific machine learning
My research interests span computational science and scientific machine learning for optimization, uncertainty quantification, and optimization under uncertainty. I am currently developing new multifidelity methods for uncertainty quantification and optimization under uncertainty. I create methods for utilizing multiple information sources in various use-inspired work to make optimization and uncertainty quantification feasible.
My doctoral research mostly focused on implications of cost of evaluating a system in Bayesian optimization. I also worked on designing flapping wing of micro air vehicle tackling the excessive cost of experiments used to estimate thrust production through parallel Bayesian optimization. During my PhD, I spent the summer of 2013 as a visiting researcher at Ecole Nationale Superieure des mines de Saint-Etienne in France working on estimating feasibility of a design under complex constraints using multiple surrogates and ROC curves.