Undergraduate Teaching

MIE363: Resource and Production Modelling

MIE363 is a core course for the undergraduate students in the third year of our Industrial Engineering program and provides students with the understanding of important issues and their remedies in Operation Management. Specific topics include demand forecasting, long-term capacity planning, inventory management, production system control and supply chain management. Emphasis has been placed on application of course materials to real life situations. To this end, the textbook materials are enhanced with a semester-long simulation game.


  1. Forecasting
  2. Capacity planning
  3. Inventory management
  4. Production system control: MRP and JIT
  5. Network optimization (computational project)
  6. Supply chain management

MIE567: Dynamic and Distributed Decision Making

This course is to provide fundamental concepts and mathematical frameworks for sequential decision making of a team of decision makers in the presence of uncertainty. Topics include Markov decision processes, reinforcement learning, theory of games, multi-agent reinforcement learning and decentralized Markov decision processes. The course is technical by nature and for advanced students with strong mathematical background and programming skills.


  1. Markov decision processes
  2. Reinforcement learning
  3. Stochastic games
  4. Multi-agent reinforcement learning
  5. Decentralized Markov decision processes

Graduate Teaching

MIE1615: Markov Decision Processes

This course covers the mathematical foundations of the Markov decision processes such as Banach fixed point theorem, and contraction mapping, as well as the standard solution methods such as the value iteration and the policy iteration. It also addresses structural properties of optimal policy in relation with mathematical characterization of the value function and introduces the simulation-based method and the reinforcement learning.


  1. Fundamental concept for the Markov decision processes
  2. Structural properties of optimal policy
  3. Standard methods: the value iteration and the policy iteration
  4. Simulation-based optimization
  5. Reinforcement learning

APS1022: Financial Engineering II

APS1022 is co-taught by Professor Kwon and myself. The first week of the cover is covered by Professor Kwon and topics include the interest theory, fixed-income securities, the term structure of interest rates, mean-variance theory and the capital asset pricing model. I teach in the second week to cover topics listed below:


  1. Arbitrage and pricing theory
  2. Risk neutral probability and risk‐free rate
  3. Asset dynamics, lattice and Monte Carlo simulation
  4. Derivatives: forwards, futures and options
  5. Hedging
  6. Option theory in detail

APS1017: Supply Chain Management

This course is to provide students with a framework to design and control supply chain systems. To achieve the goal, the course will cover key modules in supply chain. The students will be exposed to topics such as: product and supply chain matching, forecasting, inventory models, supply chain coordination via contract design, and the value of information.


  1. Supply chain types and product types
  2. Key topics in supply chain management
  3. Inventory management
  4. Multi echelon inventory models
  5. Network optimization (computational project)
  6. Transportation
  7. Value of information
  8. Contract design
  9. Value of information