tl;dr:
My primary research develops human-centered deep learning tools for creative audio workflows – enabling practitioners to create and shape sounds through systems and interfaces where the link between user input and sonic output is intuitive, expressive, and controllable.
Outside of this, I also look at how algorithmic systems reshape musical experience, bridging methods from HCI and computational musicology to examine questions of influence attribution, algorithmic mediation, and the broader social impacts of how we collectively listen to and engage with sound.
A few select projects:
For a full list of my publications, please check out Google Scholar or my CV.
Expressive, Controllable Deep Learning Tools for Creative Audio Production
- Smorph (ISMIR 2026) - playable timbral palettes via diffusion-based sound morphing
- Mix2Morph (ICASSP 2026) - repurposing TTA diffusion models for sound morphing
- Text2FX (ICASSP 2025) - How can we control audio FX like EQ and reverb using natural language descriptions instead of technical parameters?
Interfaces
- FXplorer (NIME 2026) - an interactive space for exploratory audio FX design
- Text2EQ (ISMIR LBD 2024) - human-in-the-loop interface exploring interactive refinement of text-guided audio processing
Music Listening in the Age of Algorithms
- SLIME: Streamed Listener Index of Musical Experience (ongoing) — A collaboration with NOISE lab, This work examines how recommendation systems on streaming platforms shape musical discovery and listening behavior, aiming to understand algorithmic mediation’s impact on musical culture and listening practices (presented at Clouds, Streams, and Ground Truths 2026; SMPC 2026).