Prof. Cheng-Zhi (Anna) Huang

Professor Huang is interested in taking an interaction-driven approach to designing Generative AI, to enable new ways of interacting with music (and AI) that can extend how we understand, learn, and create music. She aims to partner with musicians, to design for the specificity of their creative practice and tradition, which inevitably invites new ways of thinking about generative modeling and Human-AI collaboration.

Huang proposes to use neural networks (NNs) as a lens onto music, and a mirror onto our own understanding of music. She is interested in music theories and music cognition of NNs and for NNs, to understand, regularize and calibrate their musical behaviors. She aims to work towards interpretability and explainability that is useful for musicians interacting with the AI system. Huang envisions working with musicians to design interactive systems and visualizations that empower them to understand, debug, steer, and align the generative AI’s behavior.

Professor Huang is also interested in rethinking generative AI through the lens of social reinforcement learning (RL) and multi-agent RL, to elicit creativity not through imitation but through interaction. This framework invites us to consider how game design and reward modeling can influence how agents and users interact. She envisions a jam space, where musicians and agents can jam together, and researchers can swap in their own generative agents and reward models, similar to OpenAI’s Gym. The evaluation is not only on the resulting music, but also on the interactions, how well agents support other players. I’m also interested in efficient machine learning, to build instruments and agents that can run in real-time, to enable Human-AI collective improvisation.