Experience

2024-Present — OpenAI

I’ve worked on reinforcement learning, small models, and synthetic data.

Public contributions. My research influenced these models:

• o3 & o4-mini
• 4.1-nano & 4.1-mini
• o3-mini
• o1-mini
• 4o-mini, audio

2022-2023 — Hudson River Trading (HRT) AI Labs

I studied deep learning for high-frequency trading.

2021-2022 — Facebook AI Research (Meta AI)

I did exploratory research on how to imbue Decision Transformer with new capabilities, such as multi-task, in-context learning, and online exploration.

2018-2021 — UC Berkeley

Research. I studied the universal power of sequence modeling, sequential decision making, and how to leverage offline data for reinforcement learning.

• First author of:
  • Decision Transformer
  • Pretrained Transformers as Universal Computation Engines
  • Reset Free Lifelong Learning with Skill-Space Planning
• Supporting author on several other papers: Google Scholar
• Summary: Towards a Universal Decision Making Paradigm

Teaching. I also spent a significant portion of my undergrad life teaching.

• EECS 126 (Probability and Random Processes)
  • Head TA: Spring 2021 and Fall 2020
  • TA: Spring 2020 and Fall 2019
• CS 70 (Discrete Math and Probability)
  • Reader: Spring 2019
  • CSM Mentor: Spring 2019

Other

Email: matches my arxiv papers

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