Michael Zbyszyński is a lecturer in the Department of Computing, where he teaches perception & multimedia computing, live electroacoustic music, and real-time interaction. His research involves applications of interactive machine learning to musical instrument design and performance.
As a musician, his work spans from brass bands to symphony orchestras, including composition and improvisation with woodwinds and electronics. He has been a software developer at Avid, SoundHound, Cycling ’74, and Keith McMillen Instruments, and was Assistant Director of Pedagogy at UC Berkeley’s Center for New Music and Audio Technologies (CNMAT). He holds a PhD from UC Berkeley and studied at the Academy of Music in Kraków on a Fulbright Grant. His work has been included in Make Magazine, the Rhizome Artbase, and on the ARTSHIP recording label.
- Interactive Machine Learning
- Electroacoustic composition and improvisation
Conference or Workshop Item
The Effect of Co-adaptive Learning & Feedback in Interactive Machine Learning
Zbyszynski, Michael; Di Donato, Balandino and Tanaka, Atau. 2019. 'The Effect of Co-adaptive Learning & Feedback in Interactive Machine Learning'. In: ACM CHI: Human-Centered Machine Learning Perspectives Workshop. Glasgow, United Kingdom 4 May 2019.
Write once run anywhere revisited: machine learning and audio tools in the browser with C++ and emscripten
Zbyszynski, Michael; Grierson, Mick; Yee-King, Matthew and Fedden, Leon. 2017. 'Write once run anywhere revisited: machine learning and audio tools in the browser with C++ and emscripten'. In: Web Audio Conference 2017. Centre for Digital Music, Queen Mary University of London, United Kingdom 21-23 August 2017.
Rapid Prototyping of New Instruments with CodeCircle
Zbyszynski, Michael; Grierson, Mick and Yee-King, Matthew. 2017. 'Rapid Prototyping of New Instruments with CodeCircle'. In: New Interfaces for Musical Expression. Copenhagen, Denmark 15-18 May 2017.
Interactive Machine Learning for End-User Innovation
Bernardo, Francisco; Zbyszynski, Michael; Fiebrink, Rebecca and Grierson, Mick. 2016. 'Interactive Machine Learning for End-User Innovation'. In: Designing the User Experience of Machine Learning Systems. Palo Alto, California, United States 27-29 March 2017.