Lectures and appropriate assignments on fundamental or applied topics of current interest in industrial engineering and operations research.

This course will examine several topics at the interface of simulation, stochastic modeling, data-driven decision-making, and online learning. For simulation, emphasis is on the theoretical foundations of simulation methodology. For stochastic modeling, emphasis is on continuous-time stochastic models that are key to the areas of service systems, financial markets, and supply chain systems. For data-driven decision-making and online learning, emphasis is on developing sequential decision making policies with reliable performance guarantees. Connections are drawn among the theory of simulation, stochastic modeling, and learning. This course is aimed towards Ph.D. students, M.S. students, and MEng or undergraduate students with sufficient background and interest. Prerequisites: Introductory courses to probability, stochastic processes, statistics, and optimization. Background in linear algebra, multivariable calculus, and (ideally) the basics of real analysis. Numerical computing in a language such as Python will be required.

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