Cheng-Zhi Anna Huang

  • Robert N. Noyce Career Development Professor
  • Associate Professor of Music
  • Associate Professor of Electrical Engineering and Computer Science
Email
huangcza@mit.edu
Office
W18-2309

Anna Huang is the Robert N. Noyce Career Development Professor at MIT holding a shared appointment between MIT Music & Theater Arts Section and Electrical Engineering and Computer Science (EECS). Previously, she spent eight years with the Magenta team at Google Brain and Google DeepMind, spearheading efforts in generative modeling, reinforcement learning, and human-computer interaction to support human-AI partnerships in music-making.

Huang is the creator of the ML model Coconet that powered Google’s first AI Doodle, the Bach Doodle. In two days, Coconet harmonized 55 million melodies from users around the world. In 2018, she created Music Transformer, a breakthrough in generating music with long-term structure, and the first successful adaptation of the transformer architecture to music. Her ICLR paper is one of the most cited papers in music generation.

Huang held a Canada CIFAR AI Chair at Mila – Québec AI Institute and an adjunct professorship at Université de Montréal. She did her PhD at Harvard University and was a recipient of the NSF Graduate Research Fellowship. Huang has a master’s from the MIT Media Lab, and a dual bachelor’s from University of Southern California in music composition and CS.

As a composer, Huang has written for soloists, mixed chamber groups and orchestra, as well as tape and live electronics. Her electronic work has been performed on the 40-channel HYDRA loudspeaker orchestra. Her compositions have won awards including first place in the San Francisco Choral Artists’ a cappella composition contest. She is part of the founding team of the AI Song Contest, served as one of three inaugural judges in 2020, and a core organizer for 2021 and 2022. She has served as guest editor for ISMIR’s flagship journal, proposing and co-editing TISMIR’s special issue on AI and Musical Creativity.

She takes 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.

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