I’m a PhD Student at UC Berkeley advised by Joseph Gonzalez and Sanjit Seshia. My current work focuses on the use of Machine Learning for repetitive optimization found in software systems and in logistics. I’m particularly interested in the automatic detection and exploitation of approximate symmetries in the design of data-driven optimizers. I collaborate closely with Yuandong Tian at FAIR on this pursuit.

In 2019-2020, I served as a research assistant in the Computer Science department at Columbia University working under Ronghui Gu and Suman Jana. Specifically, my focus was in designing machine learning techniques for automating deductive program verification in hopes of scaling the benefits of formal methods to large scale software systems. I previously graduated with a BS from the same department in 2019 and graduated high school from Germantown Academy in the suburbs of Philadelphia.

Current Projects:

  • Ashera :: We built an Optimization Modulo Theory solver targeting problems such as multi-agent Traveling Salesman Problem and multi-resource task DAG scheduling. (preprint)
  • Co-creativity Companion for Writing Novel Plots :: In collaboration with Meta, we designed a Language Model (LM) companion that provides creative suggestions for long-form story writing. In our framework, we finetune a diversity-dedicated suggestion model which produces suggestion useful for both automatic editing with a general-purpose language model or a human author.
  • Automated Refactoring :: We are developing a framework for refactoring code. We are specifically focused on the technical challenges of incomplete natural language specifications provided as comments or docstrings, and automatically inferring the implicit contract between helper functions and the function caller.

Publication:

Student Forum:

Personal:

  • When I'm not busy climbing the social ladder, I enjoy sports climbing, bouldering, and skiing. I am a proud member of Noteworthy A Capella at UC Berkeley. Check out our most recent recording: