Currently, I am transitioning into a Research Scientist role at Oracle Labs to work on coding agents, following a DPhil (PhD) at the University of Oxford. During my DPhil in the WhiRL lab, I was supervised by Shimon Whiteson, funded by the Oxford-Google DeepMind Doctoral Scholarship, and studying deep reinforcement learning (RL). My main research area was meta-RL. (For an introduction, see this  tutorial  or this interview where I explain meta-RL!) I've worked on hypernetworks, initialization methods, and sequence models for in-context learning – in addition to authoring a survey of meta-RL. Previously, I did my MS and BS at Brown University, completed a pre-doc at Microsoft Research on sequence models in RL, and researched autonomous vehicles in both academia and industry.

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At Brown University, I was advised by Michael Littman. My research focused on human feedback, imitation learning, and multi-agent game theory. Some of our work in the self-driving car lab gained publicity in New Scientist. Other projects included: an RL agent in Minecraft using emotion detection as feedback and a GAN to reconstruct corrupted images. As a TA for the first iteration of Brown's graduate-level deep learning course, I designed a lab and gave a guest lecture on sequence-to-sequence machine translation.

 

In industry, I worked at Microsoft, Lyft, Adobe, and several smaller companies. I completed a pre-doc at Microsoft Research with Katja Hofmann on long-term memory in RL. At DeepScale, acquired by Tesla, I worked on perception for autonomous vehicles, developing novel methods for instance segmentation. At Lyft I designed a framework for sequential decision-making problems, including a special-case solver specific to autonomous vehicles at stop intersections. At Adobe I built neural networks to forecast marketing data. I also worked at a robotics startup on software and hardware, co-created Food with Friends (an iOS app), and researched in-context learning with large language models (LLMs) for proteins at InstaDeep.

 

Broadly, my interests include generalization, adaptation, and representation. Specifically, topics include learning to learn (in-context), sequence models, and few-shot learning – including intersections with multi-agent RL and offline learning. I am also interested in human (and AI) feedback, vision-language models for environment design, and any challenging problem in ML. Please see my CV, Google Scholar, and GitHub for a sample of my work!