Mark Crowley

Associate Professor
Mark Crowley

Contact information

Phone: 519-888-4567 x31464
Location: E5 4114


Mark Crowley

Biography summary

Mark Crowley is an Assistant Professor in the Pattern Recognition and Machine Intelligence group in the Department of Electrical and Computer Engineering at the University of Waterloo. He received his Ph.D. and M.Sc. in Computer Science from the University of British Columbia working in the Laboratory for Computational Intelligence, and a B.A. in Computer Science from York University in Toronto. He did a postdoc at Oregon State University working with Tom Dietterich’s machine learning group. Mark’s research focusses on algorithms, tools and theory at the intersection of Machine Learning, Optimization and Probabilistic Modelling. In particular he is interested in the challenges for traditional machine learning and optimization algorithms that arise in domains with spatial dynamics and very large amounts of data. He often works in collaboration with researchers in other fields such as sustainable forest management, ecology and resource economics. He is an active part of building the interdisciplinary Computational Sustainability research community and blogs on this topic as well as democratic reform and the impact of AI technology on society.

Research interests

  • Artificial Intelligence
  • M​achine ​Learning
  • Reinforcement Learning
  • D​ecision ​M​aking ​U​nder ​U​ncertainty
  • Probabilistic ​G​raphical ​M​odels
  • Causal Modeling
  • B​ig ​D​ata ​A​nalytics
  • ​O​ptimization
  • ​G​ame ​T​heory
  • Autonomous Driving
  • Medical Imaging
  • AI for Science
  • Material Design
  • AI for Video Games


  • 2011, Doctorate, Computer Science, University of British Columbia
  • 2005, Master of Science, Computer Science, University of British Columbia
  • 1999, Bachelor of Arts, Computer Science, York University


  • ECE 108 - Discrete Mathematics and Logic 1
    • Taught in 2020
  • ECE 406 - Algorithm Design and Analysis
    • Taught in 2023
  • ECE 457C - Reinforcement Learning
    • Taught in 2022, 2023
  • ECE 493 - Special Topics in Electrical and Computer Engineering
    • Taught in 2019, 2020, 2021
  • ECE 657A - Data & Knowledge Modelling & Analysis
    • Taught in 2020, 2021, 2022, 2023

* Only courses taught in the past 5 years are displayed.

Graduate studies

  • Not currently accepting applications for graduate students