I’m an researcher currently at OpenAI. My research interests are in universal learning mechanisms that work at scale. I graduated from UC Berkeley in 2021 where I worked with Pieter Abbeel and Igor Mordatch on reinforcement learning and sequence modeling.

Email: kzl at berkeley dot edu

Experience

2022-2023 — Hudson River Trading AI Labs

I worked on applied AI research for high-frequency trading, studying deep learning in a noisy, low-latency setting.

2021-2022 — Facebook AI Research

I worked on fundamental AI research studying how we could scale Decision Transformer with new capabilities, such as multi-task, in-context learning, and online exploration.

2018-2021 — UC Berkeley

My research focused on the universal power of sequence modeling and how to leverage offline data to for reinforcement learning.

• Co-first author of Decision Transformer
• First author of Pretrained Transformers as Universal Computation Engines
• Supporting author on several other papers (Google Scholar)
• Reviewer for ICLR, NeurIPS, ICML
• Other highlights: Towards a Universal Decision Making Paradigm

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

• Head TA for EECS 126 (Probability and Random Processes) in Fall 2020 and Spring 2021
• TA for EECS 126 in Fall 2019 and Spring 2020

Other

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