Sustainability Leadership Fellow Cohort: 2023-2024

Ph.D. Student, Department of Chemistry

Research Summary: I use techniques from machine learning, applied mathematics, and quantum chemistry to design novel renewable biofuels that are both clean-burning and sustainable. Using the results of accurate, high-throughput calculations, I build databases of molecular properties, which I subsequently use to create and train predictive machine learning models that design low-impact fuels to power future transport modalities. I also develop novel analytic techniques to tie the learned behaviors of these models back to the fundamental physics governing the complex chemical environments encountered in fuel combustion.

Advisor: Seonah Kim