I specialize in using computational methods to solve partial differential equations. My research focuses on creating higher order schemes based on finite volume and discontinuous Galerkin techniques to achieve highly accurate solutions. I have experience in applying these methods to fields such as electrodynamics, magnetohydrodynamics, and numerical simulations of flow MRI.

Furthermore, I am intrigued by exploring innovative computational frameworks for fluid flows, encompassing both compressible and incompressible scenarios. My knowledge in scientific computing includes scheme development, high-performance computing using GPGPUs, and applying these techniques to engineering and scientific problems.

In addition to my primary research, I am also fascinated by the potential of machine and deep learning techniques to solve problems in science and engineering. Specifically, I am focused on developing deep learning-based methods to solve inverse problems and apply them to real-world challenges in engineering.