After earning my Bachelor's and Master's degrees in Chemistry, I pursued my doctoral research in computational biophysics, specializing in studying the dynamics of diverse biomolecular complexes. As a Postdoctoral researcher at the University of California, Riverside, I have accumulated over 8 years of experience in this field. My research primarily involves modeling of biomolecular complexes, conducting extensive Molecular Dynamics (MD) simulations, employing enhanced sampling strategies, and utilizing graph theory-based network analysis to understand protein design complexities and explore avenues for modifying functionality.With proficiency in Python programming and shell scripting, I am actively interested in exploring the application of machine learning in the field of biophysics. I am currently working on the CRISPR-Cas enzyme systems, renowned for their programmable genome editing capabilities. My research aims to identify allosteric hotspots for functional regulation, narrowing down key targets, and designing variants aimed at enhancing efficacy. By combining my expertise in computational biophysics, molecular simulations, and protein design strategies, along with a strong passion for continuous learning, my goal is to drive breakthroughs in bioengineering and pharmaceutical research through innovative approaches.