معرفی
Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, with a cross-appointment in the Cheriton School of Computer Science. He is actively involved in the Waterloo Artificial Intelligence Institute (WAII), the Waterloo Institute for Complexity and Innovation (WICI), and serves as National Secretary for the Canadian Artificial Intelligence Association (CAIAC), coordinating the Canadian Conference on AI.
Research interests span the theoretical and applied aspects of Reinforcement Learning, Deep Learning, Manifold Learning, and Ensemble Methods. His work addresses challenges in domains with spatial dynamics, multi-agent systems, and uncertainty, particularly in Computational Sustainability (forest fire management, sustainable forestry), Autonomous Driving, Medical Imaging, and Material Design. Recent research focuses on integrating causal modeling with generative representation learning to improve out-of-distribution robustness in motion forecasting applications.
Key publications include foundational work on ChemGymRL environments for safe chemical process reinforcement learning, Generative Causal Representation Learning for robust forecasting, and collaborative work on multi-advisor reinforcement learning in multi-agent settings. He co-authored a textbook Elements of Dimensionality Reduction and Manifold Learning (Springer, 2023) with Prof. Ali Ghodsi and Prof. Fakhri Karray.
Teaching includes graduate and undergraduate courses in Algorithm Design, Computational Intelligence, Reinforcement Learning, and Data Modeling at the University of Waterloo since 2018. His research group has produced several notable graduates including Benyamin Ghojogh (2021), who continued as a postdoc until 2022.
