Distinguished Speaker Series: In Search of Human-AI Resonance

About the Speaker
Anna Huang is the Robert N. Noyce Career Development Associate Professor at MIT, with a shared interdisciplinary position in Music and Theater Arts (MTA) and Electrical Engineering and Computer Science (EECS). She joined MIT last Fall to help start a new graduate program in Music Technology and Computation. This year, she is the Rieman and Baketel Fellow for Music at the Harvard Radcliffe Institute. For the past decade, she has been part of the Magenta team in Google Brain and then Google DeepMind, spearheading efforts in generative modeling, reinforcement learning, and human-computer interaction. In 2017, she created Music Transformer, the first successful adaptation of the transformer architecture to music. She is also the creator of the machine learning model Coconet that powered Google Bach Doodle, which in two days harmonized 55 million melodies from users around the world. From 2020 to 2022, she was a judge and then organizer for the AI Song Contest. Before joining MIT, she held a Canada CIFAR AI Chair at Mila -- the Quebec AI Institute. Anna grew up in Hong Kong and graduated from Hong Kong Pui Ching Middle School where she learned to play the gu zang. She then went on to pursue a dual bachelor’s degree in music composition and computer science at University of Southern California. She holds a master’s from the MIT Media Lab and a PhD from Harvard University.

About the Talk
Join us for a talk by Professor Anna Huang, a leading researcher in AI and music, as she discusses her search for "Human-AI Resonance." Professor Huang, who leads the Human-AI Resonance (HAI-Res) lab at MIT, explores how creativity can emerge from collaboration between humans and AI. Her work moves beyond imitation, focusing on interaction-driven generative AI where machines learn to listen and respond to musicians in real-time. Professor Huang created the Coconet model that powered Google's first AI-powered Bach Doodle. In this talk, she will share her vision for a future where AI and musicians co-create, extending how we understand, learn, and make music together.

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