Matthew Guzdial works on creative artificial intelligence and machine learning

Learning creativity

Matthew Guzdial works on creative artificial intelligence and machine learning, an area of research that can enable machine learning to move from predicting what has come before to anticipating and creating new possibilities. His research applies AI and machine learning to domains we would typically consider requiring human creativity, such as generating content for video games, visual art and creative commentary. He has applied computational creativity for image classification and generation in a transfer learning framework (beating state of the art baselines), and built a benchmark for the development of new creative ML agents. Matthew has developed machine learning tools to support game designers, such as an intelligent game level editor with an active learning assistant, machine learning approaches for predicting user experience and tools to help with visual theming.

Matthew is a Fellow and Canada CIFAR AI Chair at Amii and an Assistant Professor of Computing Science at the University of Alberta. He achieved his Ph.D. in Computer Science in 2019 at the Georgia Institute of Technology where his thesis focused on Combinational Machine Learning Creativity. His research has been featured in popular media such as Vice, Time, Motherboard, BBC and Rolling Stone, and his code has been applied to research at Cornell University, Drexel University, Northeastern University, Worcester Polytechnic University, the University of California, Davis, and the University of California, Santa Cruz. Matthew has been an organizer for the AAAI Conference on Artificial Intelligence in Digital Entertainment and Workshop on Knowledge Extraction from Games. He has received the best paper award at the International Conference on Computational Creativity in 2017 and 2019.