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Researcher in computational materials science, machine learning, and energy materials
Recognized by the Gallogly College of Engineering at the University of Oklahoma
Recognized by the Department of Mechanical and Aerospace Engineering at Utah State University
The University of Oklahoma
Thesis focused on optimization of energy storage materials through ab-initio calculations and deep learning techniques in materials science.
Pondicherry University
Specialization in computational and condensed matter physics, with M.S. thesis on strontium doped copper ferrite nanoparticles.
Pondicherry University
Minor in Mathematics and Chemistry.
Bingham Research Center, Utah State University
Generative material design using Graph Neural Networks and Diffusion Models.
Gas emission rate modeling using CFD and machine learning techniques.
University of Oklahoma
Worked on graph VAEs, Li-ion diffusion modeling, DFT-NEB-based migration barrier data generation, and generative diffusion approaches for MXene-enabled electrodes.
Idaho National Laboratory
Implemented and optimized hp-adaptivity in finite element frameworks including LibMesh and MOOSE for high-performance scientific computing.
Utah State University
Developed Random Forest based predictive models for elemental diffusion coefficient in alloys, CO2RR DFT modeling for Ni-Mb nanoparticles based catalyst etc.
Idaho National Laboratory
Studied helium evolution and lattice thermal conductivity changes in \u03b2-Ga2O3 under irradiation using DFT and phonon-based simulation workflows.
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