Kevin Han
Principal Investigator
Kevin Han is a first-year PhD student at Carnegie Mellon University advised by Professor Amir Farimani. His research interests lie primarily in using AI for materials and drug discovery and automated scientific discovery.
He graduated from UC Berkeley with a Bachelor's in Computer Science. Previously, he worked at JPMorgan Chase's Commercial Bank AI team on LLMs as well as Lawrence Berkeley National Labs, where he published research in the prestigious Nature journal on graph neural networks for materials discovery.
At UC Berkeley, Kevin worked with Professor Gerbrand Ceder and Bowen Deng on CHGNet and large-scale inference for machine learning interatomic potentials. His areas of expertise include molecular machine learning, AI for drug discovery, AI for materials science, LLM agents, multi-agent frameworks, and scaling test-time compute.
Kevin was also the Head GSI for Berkeley's introductory programming course, where he created the RAG chatbot and other infrastructure to support the entire EECS department.
Begin Your Journey
The application takes 10 minutes and is reviewed on a rolling basis. We look for strong technical signal—projects, coursework, or competition results—and a genuine curiosity to do real research.
If admitted, you will join a structured pipeline with direct mentorship to take your work from ideation to top conference submission at venues like NeurIPS, ACL, and EMNLP.
