My primary research interest lies in the development of computational techniques for solving partial differential equations. I have been actively involved in the development of higher-order schemes based on finite-volume and discontinuous Galerkin (DG) approaches to achieve high-accuracy solutions for realistic engineering simulations. I have experience applying these techniques to various areas such as electrodynamics, magnetohydrodynamics, and flow MRI.

In addition, I am interested in exploring new computational methods for fluid flows, both compressible and incompressible. To achieve this goal, I am interested in integrating these techniques into open-source libraries and high-performance scientific computing frameworks.

In addition to my work in computational partial differential equations, I am also fascinated by data-driven approaches to addressing problems in science and engineering. Currently, I am concentrating on developing deep learning-based techniques to solve inverse problems and applying them to real-life engineering challenges.