Teaching

  • IOE 512: Introduction to Dynamic Programming –  Course Syllabus and Lectures. This introductory graduate-level course covers a wide range of topics including the fundamentals of dynamic programming and Markov decision processes with a mixture of theoretical analysis, algorithmic approaches, and applications. Special topics include partially observable Markov decision processes, reinforcement learning, and approximate dynamic programming. 

 

  • IOE543: Introduction to SchedulingCourse Syllabus and Lectures. This introductory graduate-level course covers fundamental topics in deterministic and stochastic scheduling with an emphasis on classification of problems, complexity, and analysis of exact and approximation methods applicable to many applications including manufacturing, healthcare, pattern recognition, project scheduling, and many others. 

 

  • ISE789: Introduction to Stochastic ProgrammingCourse syllabus and Lectures. This graduate course covers fundamental topics about the theory, methods, and applications of stochastic programs. 

** You are welcome to use these course materials with acknowledgment. Original power-point slides available upon request.