Sudheesh is a PhD candidate specializing in studying functional materials, with a particular focus on Metal-Organic Frameworks at the quantum scale. His research combines machine learning, density functional theory, and active learning techniques to develop surrogate models that elucidate physiochemical processes within MOFs. He is particularly interested in exploring various uncertainty quantification metrics to bolster model reliability and employs rare-event sampling methods to capture atomic events over extended time scales.
Before his current research, Sudheesh worked with Prof. Zachary Ulissi at CMU on designing high-performance ionomers for fuel cells. Outside of his academic pursuits, Sudheesh enjoys cooking, hiking/ traveling with friends, and reading about AI startups related to batteries and healthcare.
- M.S. in Chemical Engineering. Carnegie Mellon University, USA
- B.Tech. in Chemical Engineering (Honors); Minor in Computer Science. Indian Institute of Technology, Dhanbad, India