Site being updated, see my Research Lab page for current information.
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, computational sustainability, ecology, autonomous driving, physical chemistry and medical imaging.
Note for Potential Graudate Students
As of Sept 28, 2020 I have enough graduate students and I am not accepting new applications. Good luck with your search though.
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.
|Apr 16, 2021||
First news post, new website up and running...
more: First news post
- Partially Observable Mean Field Reinforcement LearningIn Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) 2021
- Active Measure Reinforcement Learning for Observation Cost Minimization: A framework for minimizing measurement costs in reinforcement learningIn Canadian Conference on Artificial Intelligence 2021
- Recognition of a Robot’s Affective Expressions under Conditions with Limited VisibilityIn 18th International Conference promoted by the IFIP Technical Committee 13 on Human–Computer Interaction (INTERACT 2021) 2021
- Isolation Mondrian Forest for Batch and Online Anomaly DetectionIn IEEE International Conference on Systems, Man, and Cybernetics (IEEE-SMC-2020) 2020
- Quantile-Quantile Embedding for Distribution Transformation, Manifold Embedding, and Image Embedding with Choice of Embedding DistributionMachine Learning with Applications (MLWA) 2021