I am a final-year Ph.D. student at Johns Hopkins University, fortunate to be advised by Prof. Mounya Elhilali.
My Research focuses on AI for understanding and generating sound. My work spans topics such as controllable generation, voice design, source separation, and detection. I enjoy bringing research to life through interactive demos, open-source tools, and datasets for the broader audio and machine learning community.
Outside of academia, I make Music as a producer, mostly hip-hop and hyperpop. This experience shapes how I approach audio research, giving me a creator's perspective on what makes audio tools expressive, controllable, and genuinely useful in the creative process.
TASLP, 2026
[TL;DR] [Paper] [Homepage] [Live Demo]
A style-captioned TTS dataset and framework enabling controllable, style-aware text-to-speech for downstream applications.
WASPAA, 2025 Spotlight
[TL;DR] [Paper] [Code] [Live Demo]
An open-vocabulary sound event detection approach that generalizes to unseen classes via flexible text-conditioned modeling.
Interspeech, 2025 Oral
[TL;DR] [Paper] [Homepage] [Live Demo]
An efficient diffusion transformer that improves text-to-audio generation quality while reducing compute.
WASPAA, 2023 Oral
A diffusion-based method for singing voice pitch correction that adjusts pitch while preserving timbre and expression.