With Music Frameworks (a hierarchical music structure representation) and new musical features, we combine music domain knowledge with deep learning, and factor music generation into sub-problems, which allows simpler models, requires less data and achieves high musicality.

Introduces new algorithms for identifying a two-level hierarchical music structure based on repetition. Automatically detected hierarchical repetition structures reveal significant interactions between structure and chord progressions, melody and rhythm. Different levels of hierarchy interact differently.

Designed a stylistic music generation system that is able to capture structure, melody, chord progression and bass styles from one or a few example music, and imitate the styles in a new piece using statistical machine learning models. 

HCMP is an emerging computer music system that can perform live music in association with human performers, with goal of creating highly autonomous artificial performers that can fill human roles

Designed series of computational models for common pipa performance techniques using "analysis-by-synthesis" method, leading to much more realistic synthesized performances

Developed mobile web app with SuperCollider that lets people use mobile gestures (speeds, ranges, directions) to control melodies (such as ringtones) in real-time as if they were playing musical instruments, adjusting pitch, volume, tempo, accompaniment and special effects etc., allowing group of people to form ringtone orchestra