Research highlights

My interests are directed towards mathematical modeling of physical phenomena and numerical simulations with relevance to engineering based problems. My research areas of expertise are in numerical analysis, finite element methods, algebraic multigrid solvers and preconditioners, numerical upscaling for porous media flow, multilevel methods for uncertainty quantification, computational fluid dynamics, and PDE-constrained optimization.

As a Research Assistant Professor at Washington University, I am working on advanced parallel numerical algorithms for large-scale inverse problems, imagining, uncertainty quantification, optimal experimental design, and optimization under uncertainty. In addition, I am interested in applying multilevel techniques to the solution of stochastic partial differential equations, including Multilevel Monte-Carlo and hierarchical sampling of spatially correlated random fields.

Find me on: Google Scholar, Academia, or Research Gate.

Bayesian Inverse Problems Optimal design of experiments
The process of extracting knowledge from data by solving inverse problems Bayesian optimal design of experiments with sparsifying penalty
Random field Optimization under uncertainty
Scalable sampling algorithms for Gaussian random fields Optimization under uncertainty: application to turbulent jet
Two-phases flow AMGe
Two-phases porous media flow Hierarchy of agglomerated meshes for element-based AMG