John Thickstun
Assistant Professor - Cornell University - Computer Science.
I work on machine learning and generative models. I’m interested in methods that control the behavior of models, both from the perspective of a user who hopes to use a model to accomplish concrete tasks, and from the perspective of a model provider or policymaker who hopes to broadly regulate the outputs of a model. I am also interested in applications of generative models that push beyond the standard text and image modalities, including music technologies.
Previously I was a Postdoctoral Scholar at Stanford University, advised by Percy Liang. I completed my PhD 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 studied Applied Mathematics as an undergraduate at Brown University, advised by Eugene Charniak and Björn Sandstede.
The MusicNet dataset has moved to permanent hosting at Zenodo.
news
Jul 1, 2024 | I am joining Cornell University as an Assistant Professor of Computer Science, starting Fall 2024! |
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Jun 17, 2024 | Hooktheory released Aria: an AI co-creator for chords and melodies powered by the Anticipatory Music Transformer. Read about it on the AudioCipher Blog. |
Mar 18, 2024 | We released a new, 780M parameter Anticipatory Music Transformer. Additional discussion here. |
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 |