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Research Interests

Arun’s research involves using first principles computational modeling, machine learning, and materials informatics to drive the design of new materials for energy-relevant applications. The primary methods used in his group are quantum mechanics-based density functional theory and regression/classification techniques based on neural networks, ridge regression and random forests. His research deals with large materials datasets relevant for solar cells and related optoelectronics, and using state-of-the-art data science approaches to create useful models and online design tools.

Education

• Ph.D. Materials Science and Engineering, University of Connecticut, 2017.
• Bachelor of Technology, Metallurgical and Materials Engineering, 2012.

Employment History

• Assistant Professor, School of Materials Engineering, Purdue University, 2020-Present.
• Postdoctoral Associate, Center for Nanoscale Materials, Argonne National Laboratory, 2017-2020.