My research seeks dependable and transparent ways to augment human decision making in complex domains in the presence of many agents, spatial structure, or uncertainty. My focus is on developing new algorithms within the fields of Reinforcement Learning, Deep Learning and Ensemble Methods, and Manifold Learning (Dimensionality Reduction) as well as other Research Topics. I often work in collaboration with researchers in applied fields such as Computational Sustainability (including Sustainable Forest Management), Autonomous Driving, AI for Science and Medical Imaging. Read more about me in my bio.
Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo and is a member of the Waterloo Artificial Intelligence Institute. He and his students carry out research into single-agent and multi-agent Reinforcement Learning large-scale 2D/3D image-like processing, and manifold learning/dimensionality reduction. Some of this research is motivated by theoretical opportunities, particularly in manifold learning and multi-agent reinforcement learning. But 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.
Besides my publications, you can find my thoughts on Artificial Intellgience, Machine Learning and how technology and science are advancing on my blog Computationally Thinking (updated intermittently) or through social media as @compthink on Twitter(at least for now) or increasingly, on Mastodon. 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.
- IOTSMSAggressive Driver Behavior Detection using Parallel Convolutional Neural Networks on Simulated and Real Driving DataIn 9th International Conference on Internet of Things: Systems, Management and Security (IOTSMS) IEEE, Milan, Italy, nov, 2022.
- Multi Type Mean Field Reinforcement LearningIn Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) International Foundation for Autonomous Agents and Multiagent Systems, London, United Kingdom, 2020.
- Robot RescueUsing Affect as a Communication Modality to Improve Human-Robot Communication in Robot-Assisted Search and Rescue ScenariosIEEE Transactions on Affective Computing. arXiv, nov, 2022.
- Upcoming Book(to appear) Elements of Dimensionality Reduction and Manifold LearningSpringer Nature, dec, 2023.
- Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningIn Proceedings of the 22nd International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), London, United Kingdom, sep, 2023.