As well as collaborating with other organisations and academics, Fast Familiar undertake independent research, in line with our own interests.
As well as being one of Fast Familiar’s lead artists, Dan Barnard is a Senior Lecturer at London South Bank University, where he leads the Digital Performance Research Group. His research interests centre around the role that digital technology can play in transforming performance and the act of spectatorship. He also writes about the insights that interactive performance can provide about the law.
FF co-lead artist Joe McAlister leads the modules “Programming for artists and designers” and “Data and machine learning for creative practice” at Goldsmiths, University of London, (MA/MFA Computational Arts, BSc Creative Computing and BSc Digital Arts Computing degrees). He is undertaking a PhD exploring how we can use interactions with digital technology to mediate uncomfortable or complex moral decisions.
Kris De Meyer, one of our associates, is a neuroscientist at King’s College London. He researches how minds change and how views become entrenched. He uses these ‘brain insights’ to overcome polarisation in society, especially around controversial issues like climate change.
Most of Dan, Joe and Kris’s research relates to specific Fast Familiar projects - you can find it on those projects’ pages.
We’ll post research that stands apart from specific projects here.
Barnard, D. “Approaches to understanding and using Katie Mitchell’s Events technique in professional and pedagogical contexts” Stanislavski Studies, 2020
Barnard, D. “fanSHEN’s Looking for Love: A Case Study in How Theatrical and Performative Practices Inform Interactive Digital Narratives” in Rouse R., Koenitz H., Haahr M. (eds) Interactive Storytelling. ICIDS 2018. Lecture Notes in Computer Science, vol 11318. (Winner of the Best Short Paper award at ICIDS 2018).
Barnard, D. “Using Games Based on Giant Dice and Time Restrictions to Enable Creativity when Teaching Artistic Subjects” International Journal of Games-Based Learning 7 (3) 2017