I have extensive experience in developing numerical methods for computational fluid dynamics. My main contributions include the development of algorithms for the grid-based Finite Volume and Finite Element methods as well as the mesh-free Smoothed Particle Hydrodynamics. I have implemented these algorithms using Object-Oriented C++, Fortran, Python, and Julia programming languages. 

Pursuing my Ph.D. degree, I developed a consistent mesh-free Smoothed Particle Hydrodynamics (SPH) method for direct modeling of particulate flows. I also conducted pioneering research on Magnetorheological fluids at the particle level, where I successfully employed a fully Lagrangian approach for their simulation.

During my post-doctoral journey, I focused on developing an efficient and robust three-dimensional two-phase (liquid-gas) flow solver within the framework of the Enriched Finite Element Method (EFEM)/Level-set method. This method has been instrumental in modeling droplet dynamics, where I incorporated realistic wetting phenomena to achieve greater accuracy.

Furthermore, I have been exploring the use of multi-fidelity and Physics-Informed Machine Learning methods to improve the overall efficiency of well-established modeling techniques. By doing so, I aim to provide more effective and accessible solutions to real-world problems.