John Thickstun

Postdoctoral Scholar - Stanford University - Computer Science.

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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. My current research interests include generative models, sampling, time series, with applications to music. 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.

selected publications

  1. Dissertation
    Leveraging generative models for music and signal processing
    Thickstun, John
    University of Washington 2021
  2. ICML
    Parallel and flexible sampling from autoregressive models via langevin dynamics
    Jayaram, Vivek, and Thickstun, John
    In International Conference on Machine Learning 2021
  3. ICML
    Source separation with deep generative priors
    Jayaram, Vivek, and Thickstun, John
    In International Conference on Machine Learning 2020
  4. ISMIR
    Coupled recurrent models for polyphonic music composition
    Thickstun, John, Harchaoui, Zaid, Foster, Dean P, and Kakade, Sham M
    In International Society for Music Information Retrieval 2019
  5. ICASSP
    Invariances and data augmentation for supervised music transcription
    Thickstun, John, Harchaoui, Zaid, Foster, Dean P, and Kakade, Sham M
    In International Conference on Acoustics, Speech and Signal Processing 2018
  6. ICLR
    Learning features of music from scratch
    Thickstun, John, Harchaoui, Zaid, and Kakade, Sham M
    In International Conference on Learning Representations 2017