Nick Bryan-Kinns

Professor Nick Bryan-Kinns is Professor of Creative Computing at the Creative Computing Institute, University of the Arts London. His research explores new approaches to interactive technologies for the Arts and Creative Industries through Creative Computing, with a current focus on Human-Centred AI and eXplainable AI for the Arts.

His research interests center on making audio engineering more accessible and inclusive, championing sustainable and ethical IoT and wearables, and engaging communities with physical computing through craft and cultural heritage. He has developed and led collaborative research with institutions including Tsinghua University, Hunan University, Tongji University, Huazhong University of Science and Technology, and Ateneo de Manila University.

His recent publications demonstrate a strong trend toward Responsible AI in music creation, with a focus on reducing bias in AI music generation systems and leveraging small datasets for ethical AI applications. His work explores how AI can be made more transparent and accountable within creative practices.

• Fellow of the Royal Society of Arts
• Fellow of the British Computer Society
• Senior Member of the Association of Computing Machinery
• Recipient of ACM and BCS Recognition of Service Awards
• Multiple conference paper awards including Honourable Mentions at ACM CHI conferences
• QMUL Public Engagement Award Winner (2017, 2015)

Professor Bryan-Kinns has supervised 24 graduated PhD students and examined 19 PhD candidates. He has secured over £24 million in research funding, including recent projects such as the AHRC BRAID Fellowship on Explainable Generative AI in the BBC (£117k), AHRC BRAID Flexible Fund on Embracing Authenticity (£10k), and Innovate-UK Creative Catalyst: AI in the Music Industry (£236k). His grant portfolio demonstrates strong international collaboration, particularly with Chinese institutions.

He leads the MusicRAI research project focused on building an international community to address bias in AI music generation and analysis, and has developed an open repository of generative AI models for music. His work bridges academic research with practical applications that have been exhibited internationally at venues including Ars Electronica, the V&A, and the Science Museum.