One of Pawan Tripathi's main scientific interests is analyzing experimental material science data with advanced data science methods and practices to reveal correlations, insights, and improve predictive modeling and as a postdoc scholar in Case Western Reserve University's Solar Durability and Lifetime Extension Center, he is able to do just that.

Tripathi, who earned a dual Ph.D. degree from Indian Institute of Technology Kanpur and National Chiao Tung University in Taiwan, will develop machine learning and data science models to automate and extract scientific information using high-dimensional datasets such as x-ray diffraction and x-ray computed tomography at SDLE. Mainly focusing on data science and machine learning techniques for the prediction of material degradation and lifetime reliability, he will work with datasets from characterization facilities of synchrotron beamlines at national laboratories, including Los Alamos National Laboratory, Sandia National Laboratory and Lawrence Livermore National Laboratory.

"CWRU is a well-recognized and top-ranked research institution in the United States,” said Tripathi, who looks forward to collaborations with “eminent scientists and researchers in my field.”

A native of Sidhauli, India, Tripathi used molecular dynamics simulation and density functional theory to perform large-scale high performance computing simulations, helping him develop a better understanding of material’s behavior, processes, and associated properties as a Ph.D. student. Additionally, he used atomistic simulation techniques to analyze atomic-scale mechanisms responsible for phase transformation and mechanical behavior during his Ph.D. studies. “I have always been fascinated by materials science and engineering at the atomic scale,” said Tripathi.

When he is not in the laboratory, Tripathi enjoys reading about ancient science and learning about Indian philosophy.