John Thickstun
Postdoctoral Scholar - Stanford University - Computer Science.
I am a Postdoctoral Scholar at Stanford, advised by Percy Liang. Previously I completed a doctorate in the Allen School of Computer Science & Engineering at the University of Washington, where I was co-advised by Sham Kakade and Zaid Harchaoui. I completed my undergraduate degree in Applied Mathematics at Brown University, where I was advised by Eugene Charniak and Björn Sandstede. I work on machine learning, with a current focus on controllable generative modeling. I am also interested in applications of machine learning to artificial intelligence problems in the audio, language, and music domains. My research has been supported by a 2017 NSF graduate fellowship, and a 2020 Qualcomm innovation fellowship.
My CV is available here.
The MusicNet dataset has moved to permanent hosting at Zenodo.
news
Mar 18, 2024 | We released a new, 780M parameter Anticipatory Music Transformer. Additional discussion here. |
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Dec 7, 2023 |
Stanford HAI featured my recent work on the Anticipatory Music Transformer!
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Oct 11, 2023 | Megha and I released human-LM interaction data that we collected last year for HALIE. We wrote a blog post that documents the data release, and highlights some qualitative trends in the data that we found interesting |
Jul 30, 2023 | Rohith and I wrote a blog post about our recent work on Watermarking LLMs. I wrote a javascript implementation of the watermark detector, which is included in the post: try it out! |
Jun 16, 2023 | I wrote an introduction to the Anticipatory Music Transformer. This includes a summary of my generative music research program, samples of music generated by these models, and resources for using these models yourself. |