I am a 4th year PhD student at MIT CSAIL advised by Jacob Andreas and Julie Shah. I’ve spent wonderful summers at the Boston Dynamics AI Institute, MIT-IBM Watson AI Lab, Facebook AI Research (FAIR), and before grad school, two years as an AI Resident at Microsoft Research. I did my undergrad at Yale, where I got my start in research with Brian Scassellati and read a lot of dead philosophers.

I’m interested in building agents that learn representations from rich human knowledge, whether directly (e.g. from feedback) or through priors (e.g. from LMs). Currently, I’m thinking a lot about how to utilize pretrained model priors in conjunction with human feedback to interactively learn user-aligned representations/rewards.

A history buff at heart, I care deeply about creating a world where AI is used safely, ethically, and equitably. I prioritize connecting with non-academic communities, and have worked at the White House Office of Science and Technology Policy (OSTP) and Schmidt Futures on AI policy. I also serve on the advisory board of the Yale Jackson School of Global Affairs, where I co-teach a course on AI for policymakers.

I love being outdoors, even in the brutal Boston winters. A current goal is to run a sub-3:00 marathon (this is how I’m doing). Reach out to chat about research, policy, or running! Preferred subject line: Your cat is dope.

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  • Ph.D. Computer Science, 2023 -

    Massachusetts Institute of Technology

  • M.S. Computer Science, 2023

    Massachusetts Institute of Technology

  • B.S. Cognitive Science, 2018

    Yale University

  • B.A. Global Affairs, 2018

    Yale University

People Financially Invested in My Future

  • Open Philanthropy
  • NSF Graduate Research Fellowship
  • Truman Scholarship
  • My parents

Recent News

All news»

[Dec 2023] Attending NeurIPS! I’m presenting Human-Guided Complexity-Controlled Abstractions in the main conference and co-organizing the Goal-Conditioned Reinforcement Learning Workshop.

[Nov 2023] Our paper Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback was accepted to TMLR.

[Nov 2023] Our papers Aligning Human and Robot Representations and Preference-Conditioned Language-Guided Abstractions were accepted to HRI 2024.

[Nov 2023] Our new preprint Getting Aligned on Representational Alignment is out.

[Nov 2023] I passed my quals at MIT!

[Sep 2023] Our paper Human-Guided Complexity-Controlled Abstractions was accepted to NeurIPS 2023.


Aligning Robot Representations with Humans
Getting Aligned on Representational Alignment
Investigations of Performance and Bias in Human-AI Teamwork in Hiring
On the Nature of Bias Percolation: Assessing Multiaxial Collaboration in Human-AI Systems
Human-Machine Collaboration for Fast Land Cover Mapping
An Integrated Machine Learning Approach To Studying Terrorism
Conceptual Feasibility Study of the Hyperloop Vehicle for Next-Generation Transport
Learning with Language-Guided State Abstractions
Early Detection of Boko Haram Attacks in Nigeria