Pawan Tripathi

My current research interests are in Materials Data Science and advanced analytical techniques, focusing on spatio-temporal analytics of synchrotron beamline X-ray diffraction (XRD) and X-ray computed tomography (XCT) data. I develop automated analysis pipelines to expedite processing and interpretation of complex datasets from synchrotron experiments. Utilizing image processing and machine-learning models, I extract scientific insights, with emphasis on semantic segmentation using deep learning architectures like UNet. Additionally, I advance Materials Data Science in Advanced Manufacturing, employing methodologies to uncover correlations and enhance predictive modeling on high-dimensional datasets through image processing, deep learning, and statistical modeling. I ensure adherence to FAIR principles in Materials Data-FAIRification, developing ontologies and schemas for various domains, including additive manufacturing, XCT analysis, and XRD analysis. Furthermore, I explore Atomistic Simulation using High-Performance Computing for investigating phase transformation and deformation behavior at the atomic scale, employing large-scale molecular dynamics simulations in LAMMPS and density functional theory in VASP and Quantum-Espresso to understand material properties. Through multidisciplinary research, I aim to contribute to materials science and engineering, addressing challenges in manufacturing, characterization, and computational modeling.