bio

About Professor Mark Crowley.

I’m an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. I’m a member of the Waterloo Institute for Artifical Intelligence (waterloo.ai), the Waterloo Institute for Complexity and Innovation (WICI) and Secretary of the Canadian Artificial Intelligence Association (CAIAC) which coordinates the yearly organization of the Canadian Conference on AI. I received his Ph.D. and M.Sc. in Computer Science from the University of British Columbia working in the Laboratory for Computational Intelligence with David Poole. Before coming to Waterloo he completed a postdoc at Oregon State University working with Tom Dietterich’s machine learning group on robust decision making under uncertainty in simulated Forest Fire domains.

In my research I seek out dependable and transparent ways to augment human decision making in complex domains. That complexity is what Artificial Intelligence/Machine Learning (AI/ML) makes research so difficult as well as so exciting. The complexity could come from the presence of spatial structure, large scale streaming data, uncertainty, or unknown causal structure, or interaction of multiple decision makers.

Our focus is on developing new algorithms, methodologies, simulations, and datasets within the fields of Reinforcement Learning (RL), Deep Learning, Manifold Learning and Ensemble Methods. I often work through collaborative projects with academic researchers, industry researchers and engineers or policy-makers in diverse fields such as sustainable forest management, physics and chemistry, autonomous vehicle development and medical imaging.

More specifically, my students and I carry out research into single-agent and multi-agent Reinforcement Learning, large-scale 2D/3D image-like processing, general advances in machine learning and anomaly detection, and fundamental research into methods for manifold learning/dimensionality reduction. Some of this research is motivated by theoretical opportunities, particularly in manifold learning and multi-agent reinforcement learning. However, most of the work flows out of challenges raised by real-world domains including forest fire management, the automotive domain, medical imaging, and digital chemistry/material design.

I’m always happy to talk to people about my research, or the impact of AI/ML/RL on our world and its role in our society in the future, but I do not take on many incoming students, so potential students should read this note about joining my lab.

Third-Person, Short Bio

Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, is a member of the Waterloo Artificial Intelligence Institute, the Waterloo Institute for Complexity and Innovation (WICI) and is National Secretary of the Canadian Artificial Intelligence Association (CAIAC) which coordinates the yearly organization of the Canadian Conference on AI. He and his students carry out research into single-agent and multi-agent Reinforcement Learning, large-scale 2D/3D image-like processing, general advances in machine learning and anomaly detection, and fundamental research into methods for manifold learning/dimensionality reduction. Some of this research is motivated by theoretical opportunities, particularly in manifold learning and multi-agent reinforcement learning. However, most of the work flows out of challenges raised by real-world domains including forest fire management, the automotive domain, medical imaging, and digital chemistry/material design.

Very Short Research Statement

Prof. Mark Crowley runs the UWECEML lab and is an Associate Professor at the University of Waterloo in the ECE department. His research explores how to augment human decision making in complex domains in dependable and transparent ways by investigating the theoretical and practical challenges that arise from the presence of spatial structure, large scale streaming data, uncertainty, or unknown causal structure, or interaction of multiple decision makers. His focus is on developing new algorithms, methodologies, simulations, and datasets within the fields of Reinforcement Learning (RL), Deep Learning, Manifold Learning and Ensemble Methods.