I am a Mechanical Engineer, currently pursuing Doctoral degree at University of Connecticut. I am deeply passionate about tackling complex multi-scale and multi-physics problems through a combination of advanced computational and experimental methods. My expertise spans a wide array of advanced numerical techniques, including Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Thermal Analysis, and Fluid-Structure Interaction (FSI). Additionally, my skills in Machine Learning, Deep Learning, and Computer Vision enable me to develop state-of-the-art solutions for challenging engineering problems.

My research interests are diverse and encompass computational geometry, optimization, and data-driven models, including Physics-Informed Neural Networks (PINNs), Neural Operators, and Bayesian Neural Networks. I am also deeply engaged in Parallel Computing with GPU technology and sophisticated randomized algorithms. My focus is on optimizing design processes and manufacturing techniques, and improving failure prediction through these advanced computational methods. Additionally, I explore probabilistic computational mechanics and multi-scale uncertainty quantification to address the complexities of modern engineering challenges. As a dedicated problem solver, I am committed to collaborating with diverse teams to deliver impactful results and push the boundaries of engineering forward.