I joined the De Novo team at Prescient Design in 2023. For my Ph.D., I used approaches in experimental and computational biophysics to study the mechanisms underlying force-dependent protein interactions. I became interested in developing predictive models that combine physics and ML, which I explored as an intern at Microsoft Research.
eLife (Research Advance) 2022.
Journal of Molecular Biology 2022.
At Prescient, I apply my interdisciplinary background in biophysics, structural biology, and machine learning to address challenges in de novo protein design. I am deeply interested in integrating physics-based and data-based approaches to develop generalizable models for protein property prediction.