I am a dedicated and highly motivated researcher with a strong background in Computational Fluid Dynamics (CFD), Reduced Order Modeling (ROM), and Machine Learning. My work focuses on solving complex aerodynamic and propulsion problems, blending theoretical foundations with cutting-edge computational techniques.I am pursuing an M.Tech in Aerospace Engineering at IIT Bombay, specialising in Aerodynamics. My M.Tech Thesis is on ROM for convection-dominated flows. My projects leverage tools like OpenFOAM, SU2, and Python to develop scalable solutions for problems like transonic store separation, scramjet inlet dynamics, and airfoil performance.Key Areas of Expertise:ROM: Proficient in ROM techniques, including Proper Orthogonal Decomposition, and Galerkin projection to predict solutions at unsampled parameters.Numerical Methods: Coded moving grid simulations using Finite Volume Method (FVM) for dynamic mesh cases.High-Performance Scientific Computing: Course experience in OpenMP, MPI, and CUDA for parallel computing, with skills in profiling and optimizing numerical simulations for large-scale computations.Machine Learning: Experience with supervised and unsupervised learning, regression models (Random Forest, XGBoost), and advanced techniques like Physics-Informed Neural Networks.Aerodynamics: Conducted detailed studies on dynamic stall, airfoil and wing analyses, and axial compressor design using CFD and analytical methods.Highlights from My Work:Ranked 36th in GATE Aerospace 2023.Best Paper Award at MRAE2023 for publishing "Dynamic Stall of a Pitching Airfoil: Comparative Study with ONERA and OpenFOAM", in SpringerLink.Research Assistant for the OpenFOAM team at CFD-FOSSEE, IIT Bombay, creating intermediate to advanced tutorials and mentoring projects.I am eager to bring my expertise to the aerospace industry, contributing to cutting-edge research and innovative solutions in aerodynamics, propulsion, and beyond. Let’s connect to explore potential collaborations!