I am a (happy!) graduate student in Julie Shah's Interactive Robotics Group (IRG) at MIT. In my research, I focus primarily on using cognitively-motivated principles to analyze and guide language-related neural networks. This manifests itself as (causal) probing methods to understand the linguistic properties of pre-trained LLMs and inducing more human-like emergent communication. I work with colleagues in the EECS, BCS, and Aero/Astro departments at MIT.

Before joining IRG, I worked for two years as a software engineer on the Advanced Projects team at Amazon Robotics. Prior to that, I got my Masters in Engineering in the Robust Robotics Group at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, concentrating in robotics. My research adviser was Professor Nicholas Roy in the Department of Aeronautics and Astronautics.

Prior to joining CSAIL as a graduate student, I earned my BS in Electrical Engineering and Computer Science and Aeronautical and Astronautical Engineering from MIT in 2015.


I am on the academic job market! I am considering both postdocs and faculty positions, in the US and Europe. Please contact me if you have any relevant openings and want to chat.

I will be at NeurIPS 2023 presenting my work on "Human-Guided Complexity-Controlled Abstractions" in the main conference and "Increasing Brain-LLM Alignment via Information-Theoretic Compression" at the UniReps workshop! I'd love to meet in person.

NeurIPS 2022

Some of the my most recent work explores how trading off utility, informativeness, and complexity can be used to generate human-like emergent communication systems (example for a color domain shown on the left).