MIE 1605H: Stochastic Processes: (2026 Syllabus) Introduction to fundamental probabilistic models and their applications. Topics include discrete Markov chains, Poisson processes, Continuous-time Markov processes, renewal theory, Martingales, Brownian motion and Diffusion processes.
MIE 1613H: Stochastic Simulation: (2027 Syllabus) This course is a graduate level introduction to methods for modelling, analyzing, and optimizing complex stochastic systems using Monte Carlo and discrete-event simulation. The course provides a rigorous yet accessible treatment of the probabilistic and statistical foundations of simulation and emphasizes its integration with learning and optimization for decision-making under uncertainty. Concepts and methods are illustrated through various practical applications, including service and healthcare systems, sharing-economy platforms, inventory and supply chain systems, and financial engineering.
Undergraduate
MIE237H: Statistics: This course is an introduction to statistical thinking and methods for second-year Industrial Engineering (IE) students. It focuses on the core tools of frequentist Statistics including estimation, hypothesis testing, confidence intervals, linear regression, and principles of experimental design, while emphasizing how these methods support evidence-based decision making in IE applications.
MIE 360H: Systems Modelling and Simulation: This course is an introduction to modelling and analysis of complex stochastic systems using computer simulation. Simulation finds applications in various areas including manufacturing, service, healthcare, finance, and public policy. Broadly speaking, simulation models allow decision makers to evaluate systems that do not yet exist, and to answer what-if questions about them, e.g., How much will wait time on average be reduced if an emergency department adds another physician to the night shift? What is the expected increase in profit during the next quarter if a new investment strategy is employed? How many police patrol cars are required to ensure that the response time to a call is below 5 minutes, 90% of the time? The focus of this course will be on stochastic simulation and decision making under uncertainty. In particular, it covers topics termed Monte Carlo and Discrete-Event simulation
