I am a chemical engineer, computational materials researcher, and scientific Python engineer specializing in DFT, atomistic simulation, machine learning, and reproducible software. I have served as Co-Principal Investigator and Computational Lead in funded energy-storage R&D and contributed to industrial simulation projects. My expertise includes Quantum ESPRESSO, ASE, NEB/CI-NEB, ML interatomic potentials, active learning, uncertainty quantification, and materials screening. I develop ActiStruct, NEBWalk, and MLIPForge for verified materials discovery and reaction-path workflows. My interests span energy storage, hydrogen, catalysis, 2D and porous materials. I seek sponsored R&D partnerships linking predictive modelling, experimental validation, and engineering decisions.