Matthew Guzdial focuses on how AI can help support and empower human creativity.
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. In 2025, he was named Science Fellow at The Edmonton Space & Science Foundation (ESSF). 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 focuses on the intersection of artificial intelligence, creativity, and human-computer interaction, primarily in the domain of games. This includes investigating automated content generation, human-AI interaction, and transfer learning. He is a recipient of an Early Career Researcher Award from NSERC and two best paper awards from the International Conference on Computational Creativity. His work has been featured in the BBC, CBC, MIT Technology Review, and WIRED.
Matthew has received the best paper award at the International Conference on Computational Creativity in 2017 and 2019.
