Scholar
Jonah Casebeer
Google Scholar ID: QwAo-K4AAAAJ
Adobe Research
Citations & Impact
Citations
315
H-index
9
i10-index
8
Publications
20
Co-authors
24
list available
Publications
Resume (English only)
Academic Achievements
- Published several papers including 'DRAGON: Distributional Rewards Optimize Diffusion Generative Models', 'Re-Bottleneck: Latent Re-Structuring for Neural Audio Autoencoders' (Best Paper Award), 'Learning to Upsample and Upmix Audio in the Latent Domain', etc.
Research Experience
- Works as a research scientist at Adobe Research, focusing on the application of machine learning, deep learning, and signal processing in the field of audio.
Education
- Received his Ph.D. in Computer Science and B.S. in Statistics and Computer Science from the University of Illinois Urbana-Champaign, where he was advised by Prof. Paris Smaragdis.
Background
- A research scientist in the Music AI group at Adobe Research. His research focuses on exploiting natural structure in data and algorithms to achieve efficiency across multiple axes: computational cost, data requirements, user effort, and downstream model performance. He especially enjoys applications in audio.
Miscellany
- Enthusiastic about collaborating with students through research internships at Adobe Research, which typically span 3-4 months.
