re-visioning.

the act of rendering practices/ thoughts/ systems/ frameworks as (in)tangible objects

Jon He, Ph.D is a researcher in sound and interactive media systems, specialising in computational methods for technologically mediated performance, instrument design, and human–computer interaction (HCI). His work focuses on integrating sensing technologies, digital signal processing, machine learning, and mechatronics to enable new forms of musical expression, analysis, and performance. A key strand of his research investigates ancient sonic practice as frameworks for interface and interaction design, contributing methodological approaches for embedding cultural knowledge into interactive systems.

Jon is a Senior Lecturer in Music Technology (Hardware and Software) at the School of Music and Creative Media Production, Massey University (Wellington, New Zealand). His teaching and supervision focus on physical computing, embedded audio systems, digital signal processing, and interaction design. Prior to joining Massey University, he was a Postdoctoral Teaching Fellow in New Media (Sonic Arts) at Yale-NUS College, Singapore.

He holds a BA (Hons) in Music Technology from LASALLE College of the Arts (Singapore, 2010). In 2011, he completed a residency at the Studio for Electro-Instrumental Music (STEIM), focusing on electronic instrument design for performance. Supported by the CalArts Music Technology Scholarship, he completed an MFA in Music Technology: Interaction, Intelligence and Design, with a concurrent concentration in Integrated Media, at the California Institute of the Arts (USA, 2013). During this time, he contributed to a National Science Foundation initiative integrating computer science concepts into digital media arts curricula and was a researcher on a Google Research Award project developing remote sensing devices for real-time audio feature extraction and visualisation.

Jon completed his Ph.D in 2017 under Dr Ajay Kapur and Prof Dale A. Carnegie at Victoria University of Wellington (New Zealand). His doctoral research developed computational tools and gesture recognition systems based on machine learning techniques to support the analysis, documentation, and interactive mediation of guqin music. This work advanced methods for coupling gestural sensing, signal analysis, and interactive media for both research and performance. He received the Postgraduate Research Excellence Award and the Laywood and Joyce Chan Award (2015) for History/Culture, recognising methodological innovation and research impact.