Designing AI music systems that enhance creativity, support performance, and connect human expression with machine learning.
My work focuses on building impactful AI music systems grounded in real musical practice.
I explore generative music models, machine-learning–based audio modeling,
human–AI co-creativity, and interactive tools for rehearsal,
performance, and learning.
I aim to create systems that enhance musicians’ creativity and musical understanding—never replacing
artistry, but supporting expressive, collaborative workflows.
ACM UIST 2025 (Accepted)
An AI-assisted rehearsal tool that visualizes pitch, rhythm, and dynamics to help novice a cappella singers practice more effectively with group recordings and receive structured feedback.
PaperISMIR 2025 Late-Breaking Demo (Accepted)
A studio-quality, multilingual a cappella dataset and experimental pipeline for two-step source separation, focusing on vocal percussion isolation and SATB separation for more realistic rehearsal and production scenarios.
PaperNeurIPS 2025 — AI for Music Workshop (Accepted)
A 55-song multilingual a cappella dataset (SATB + vocal percussion) recorded by professional ensembles, designed for training and evaluating models in source separation, rehearsal support, and AI music applications.
PaperA browser-based demo showcasing AI-assisted rehearsal features powered by ACappellaSet, including SATB visualization, vocal percussion extraction, and practice feedback tools for a cappella singers. Built in collaboration with Kexin Phyllis Ju and Prof. Hao-Wen Dong.
I’m currently refining my full CV. If you’d like the most up-to-date version or more details about my work, please feel free to reach out via email.