Bob L.T. Sturm (KTH)

I am the PI of the MUSAiC project (ERC-2019-COG No. 864189). Before becoming an Associate Professor of Computer Science at KTH in 2018, I was a Lecturer in Digital Media at the Centre for Digital Music, School of Electronic Engineering and Computer Science, Queen Mary University of London. Before joining QMUL in 2014, I was a lektor at the Department of Architecture, Design and Media Technology, Aalborg University Copenhagen. Before joining AAU in 2010, I was a postdoc at “Lutheries – Acoustique – Musique” (LAM) de l’Institut Jean le Rond d’Alembert, Paris 6. I received my PhD in Electrical and Computer Engineering in 2009 from the University of California, Santa Barbara.

Keywords: machine listening for music and audio, evaluation, music modeling and generation, ethics of AI, machine learning for music, digital signal processing for sound and music, folk music, Irish traditional music, English Morris dancing, Scandinavian folk music, accordion, caricature, painting

Chris White (UMass)

Christopher White is Associate Professor of Music Theory at the University of Massachusetts Amherst, having previously taught at The University of North Carolina at Greensboro and Harvard University. Chris received his PhD from Yale University and has also attended Queens College–CUNY, and Oberlin College Conservatory of Music.

Chris’s research uses big data and computational techniques to study how we hear and write music. He has published widely in such venues as Music Perception, Music Theory Online, and Music Theory Spectrum. His first book The Music in The Data (2022, Routledge) investigates how computer-aided research techniques can hone to how we think about music’s structure and expressive content.  His second book, The AI Music Problem (2025, Routledge) outlines ways that music poses difficulties for contemporary generative AI. He also had published in popular press venues— including Slate, The Daily Beast, and The Chicago Tribune—on a wide range of topics, including music analysis, computational modeling, and artificial intelligence. Chris also remains an avid organist, actively performing and collaborating across New England.

(Photo by Eric Berlin)

Anna Huang (MIT)

In Fall 2024, I started a faculty position at Massachusetts Institute of Technology (MIT), with a shared position between Electrical Engineering and Computer Science (EECS) and Music and Theater Arts (MTA). For the past 8 years, I have been a researcher at Magenta in Google Brain and then Google DeepMind, working on generative models and interfaces to support human-AI partnerships in music making. 

I am 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, I created Music Transformer, a breakthrough in generating music with long-term structure, and the first successful adaptation of the transformer architecture to music. Our ICLR paper is currently the most cited paper in music generation. 

I was a Canada CIFAR AI Chair at Mila, and continue to hold an adjunct professorship at University of Montreal. I was a judge then organizer for AI Song Contest 2020-22. I did my PhD at Harvard University, master’s at the MIT Media Lab, and a dual bachelor’s at University of Southern California in music composition and CS.