Mark Crowley

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.

• Artificial Intelligence and Machine Learning
• Probabilistic Graphical Models
• Decision Making Under Uncertainty
• Big Data Analytics
• Optimization
• Reinforcement Learning
• Game Theory
• Autonomous and connected car
• Automotive
• Operational Artificial Intelligence

• 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 Math and Logic 1
  • Taught in 2020
• ECE 150 - Fundamentals of Programming
  • Taught in 2017
• ECE 493 - Special Tpcs in Elec &Comp
  • Taught in 2019, 2020, 2021
• ECE 499 - Engineering Project
  • Taught in 2020, 2021
• ECE 656 - Database Systems
  • Taught in 2018
• ECE 657 - Tools of Intellignt System Des
  • Taught in 2021
• ECE 657A - Data & Knwldg Mdelling & Anlys
  • Taught in 2017, 2018, 2020, 2021
• SE 102 - Seminar
  • Taught in 2020
• SE 201 - Seminar
  • Taught in 2020
• SE 202 - Seminar
  • Taught in 2021

• Currently considering applications from graduate students. A completed online application is required for admission; start the application process now.