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Matthew Guzdial

Matthew Guzdial

Appointment

Canada CIFAR AI Chair

Pan-Canadian AI Strategy

Connect

University of Alberta

Google Scholar

About

Appointed Canada CIFAR AI Chair – 2019

Matthew Guzdial is a Canada CIFAR AI Chair, a fellow at Amii, and an assistant professor of Computing Science at the University of Alberta.

Guzdial’s research focuses on the intersection of machine learning (ML) and creativity. This leads to projects on how ML can help humans in creative endeavors like creating new video games or how ML agents can help people in a design process.  He has also worked on questions such as how models of creativity from psychology can be adapted to help machine learning. For example, Guzdial has conducted research on using a computer model combinational creativity (the type of creativity used to combine old knowledge to make something new) to allow a large music generation model to generate Iranian folk music, something it was never trained on.

Awards

  • Best Paper Award, Association for Computational Creativity, 2017 & 2019
  • Best Program Committee Member Award, AAAI Artificial Intelligence and Digital Entertainment, 2018
  • Heidelberg Laureate Forum Young Researcher, Heidelberg Laureate Forum, 2018
  • Unity Graduate Fellowship, Unity Software Inc., 2018
  • Best Paper Award, Association for Computational Creativity, 2016

Relevant Publications

  • Doosti, A., & Guzdial, M. (2023). Transfer Learning for Underrepresented Music Generation. Proceedings of the Fourteenth International Conference on Computational Creativity.
  • Guzdial, M., Snodgrass, S., & Summerville, A. (2022). Procedural content generation via machine learning: An overview. Springer.
  • Halina, E., & Guzdial, M. (2022). Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence AI and Arts (pp. 4943-4949).
  • Sarkar, A., Guzdial, M., Snodgrass, S., Summerville, A., Machado, T., & Smith G. (2022). Procedural content generation via knowledge transformation (PCG-KT). In IEEE Transactions on Games.
  • Guzdial, M., Liao, N., Chen, J., Chen, S. Y., Shah, S., Shah, V., … & Riedl, M. O. (2019). Friend, collaborator, student, manager: How design of an ai-driven game level editor affects creators. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-13).

  • Summerville, A., Snodgrass, S., Guzdial, M., Holmgård, C., Hoover, A. K., Isaksen, A., … & Togelius, J. (2018). Procedural content generation via machine learning (PCGML). IEEE Transactions on Games, 10(3), 257-270.

Institution

Amii

University of Alberta

Department

Computing Science

Education

  • PhD (Computer Science), Georgia Institute of Technology
  • BSc (Computational Media), Georgia Institute of Technology

Country

Canada

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