About

I'm Ping Wang (王平), an undergraduate studying computer science at the Paul G. Allen School, University of Washington. I work with Prof. Noah A. Smith at UW and, since May 2026, with Prof. Yi-Hsuan Yang in the Music and AI Lab at National Taiwan University.

My research focuses on making generative models more controllable, adaptive, and aligned with human intentions. I am particularly interested in post-training, reinforcement learning, and multimodal learning: how models can learn from feedback, how to design meaningful reward signals, and how models can adapt through interaction. I am also interested in how these ideas extend to multi-agent systems, where agents must coordinate and respond to one another in dynamic environments.

Much of my recent work explores these questions through music and audio, including rubric-based optimization for text-to-music generation, human-aligned evaluation of music transcription, and audio-to-symbolic music understanding. Through these projects, I aim to develop methods that improve how generative models learn, understand, and interact with complex information, with applications both within and beyond music.