My research focuses on how to make large pre-trained models (e.g. GPT-3) safe and useful for real-world problems. I'm especially interested in multimodal and domain-agnostic pretraining, which has the potential to unlock important applications in the natural sciences, engineering, and healthcare.
In the past, I've also worked in reinforcement learning, human-robot interaction, and computational astronomy, and I've spent time at Google Brain, Google Language, and Google Civics.
I'm grateful to be supported by an Open Philanthropy AI Fellowship.
In Fall 2021 I was the instructor of Stanford's CS 197: Computer Science Research. (Slides and materials)
- UC Berkeley, October 2022
- MIT, October 2022
- Cornell University, September 2022
- Columbia University, September 2022
- University of Washington, June 2022, Self-Supervised Learning for the Real World
- Harvard Medical School, February 2022, DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning
- Invited Talk, NeurIPS Workshop on Controllable Generative Modeling in Language and Vision, December 2021, Off the Beaten Path: Domain-Agnostic ML for Controllable Generation and Beyond
- Stanford Center for Research on Foundation Models, October 2021, Active Learning Helps Pretrained Models Learn the Intended Task
- Stanford Vision and Learning Lab, August 2021, Towards Universal Self-Supervision
- Stanford OVAL Seminar, May 2021, Understanding and Controlling Transfer Learning in Large Language Models
- FAIR, December 2020, Language Through a Prism: A Spectral Approach for Multiscale Language Representations.
- Google Brain, September 2018, Searching for Planets with WaveNet
- NASA Ames Research Center, September 2018, Overcoming Dataset Challenges for Vetting Exoplanets with Machine Learning.
(See Essays for more)
Other topics I think a lot about:
Societal impacts of technology, especially machine learning and large language models
Scientific communication and breaking down walls between fields
Outside of research, I organize the Stanford Queer in AI Dinner with Stanford Inclusion in AI