Info
Posts
I'm currently a 3rd Year PhD student at Brown University, advised by Lorin Crawford, and a Student Researcher at Google Deepmind on the Protein Function team in the Bay Area, CA. I work on biologically and physically inspired generative models, with a focus on learning meaningful, data-efficient representations for protein-design problems.
Before that, I studied Electrical Engineering and Computer Science at UC Berkeley and graduated in 2021.
I've spent summers at IBM Research, Salesforce and Optum, working on different research and data engineering problems. Most recently, I was a Machine Learning intern at Prescient Design, where I developed principled score matching methods for biological manifolds, applied to sequence design.
I'm excited about the future of Machine Learning for scientific domains,and its applications in climate, medicine and synthetic life. My reading and work leans into the mathematical and algorithmic principles that we can use to explain structure and mechanisms in science. As I learn more about math and proteins, I'm trying to make notes and collect useful links. You can find notes here and interesting reads here. Some of the workflows that I've created as I've needed them can be found on my github.
I also spend time co-organizing a Machine Learning x Proteins seminar series and co-run after-school computational science workshops at Providence public schools with some of my wonderful peers.
As a new graduate student, I thought these resources were super useful (some Boston-area specific ones too!).