I'm a first-year graduate student at Stanford University. I'm studying computer science, interested in systems research, and am currently working with with Peter Bailis on the MacroBase project. Before Stanford, I did my undergrad at Harvard, where I majored in computer science and worked with Professor Margo Seltzer on the Automatically Scalable Computation (ASC) project, writing my senior thesis on my work on it. I also worked with Professor Alexander Rush on a natural language processing project of predicting congressional voting records from bill text, which made it into EMNLP 2016.
Bachelor's in Computer Science • 2017
I concentrated in Computer Science at Harvard, graduating in 2017. My undergrad and thesis advisor was Professor Margo Seltzer. I ended up taking and, later, being a teaching fellow for the notorious CS 161.
I worked with Professor Seltzer on the Automatically Scalable Computation (ASC) project. The aim of the project was to develop a system that automatically parallelized sequential programs by predicting future behavior of the program and then speculating from the predictions, speeding up the program with the results of the speculations if the predictions turned out to be accurate. My work substantially improved the efficacy and scalability of ASC, demonstrating real speedup on a variety of programs. This project became my senior thesis, which received highest honors and won the Hoopes Prize for excellence in undergraduate research.
I also worked with Professor Alexander Rush and my friend Hirsh Jain on a natural language processing project predicting the results of congressional votes based purely on the text of the bills voted on. We developed a novel approach to the problem, creating a powerful and simple model that both predicted votes with over 90% accuracy (higher than any previous approach) and easily lent itself to analysis, offering useful insights into the behavior of congresspeople. This work was published in EMNLP 2016.