My research seeks dependable and transparent ways to augment human decision making in complex domains in the presence of spatial structure, large scale streaming data, or uncertainty. My focus is on developing new algorithms within the fields of Reinforcement Learning, Deep Learning and Ensemble Methods. I often work in collaboration with researchers in applied fields such as sustainable forest management, ecology, autonomous driving, physical chemistry and medical imaging.
In the field of Computational Sustainability I have worked on learning predictive models of and optimizing policies for domains in invasive species control, forest harvest management and forest fire management. These types of domains offer unique challenges for traditional artificial intelligence and machine learning algorithms for decision making, prediction and anomaly detection.
Mark Crowley is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo and a member of the Waterloo Institute for Artifical Intelligence (waterloo.ai). He is also a core member of the Waterloo Institute for Complexity and Innovation (WICI). He 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 did 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.
Besides my publications, you can follow my Computationally Thinking blog or @compthink on twitter for links and thoughts on Artificial Intellgience, Machine Learning and how technology and science are advancing.