IE1082: Probabilistic Methods in Operations Research (undergrad level)

This course introduces students to probability models and operations research techniques for decision making under uncertainty by covering topics including probability review, discrete-time Markov chains, poisson processes, continuous-time Markov chains, and queueing models. Applications are drawn from a wide variety of areas including manufacturing, inventory, biology, and healthcare systems.

IE3093: Stochastic Programming (PhD level)

Stochastic programming deals with models and algorithms for optimization problems in which data are uncertain, i.e., some of the parameters are not perfectly known at the time a decision is made and the outcome depends on a future random event. This course covers theory, algorithms, and applications of stochastic programming. Topics include two-stage stochastic programming, chance-constrained programming, computational solution approaches, approximation and sampling methods, and recent advances in the field and integration with machine learning. Multi-stage problems and stochastic integer programs will also be discussed.

INDE3382: Stochastic Processes (undergrad)

This course covers how advanced concepts and techniques in probability and statistics are used for building probabilistic models that represent real-world systems and analyzing the systems in the presence of uncertainty. The applications are drawn from a wide variety of areas including manufacturing, inventory management, telecommunications, and healthcare systems. Specific topics include Poisson processes, exponential distributions, Markov chains, and queueing theory. Decision-making and analysis techniques built on stochastic processes, e.g., Markov decision processes and discrete event simulation, will also be covered.

INDE7397: Decision Modeling and Optimization under Uncertainty (grad)

The aim of this course is to introduce quantitative modeling and optimization techniques to address various types of uncertainty in decision-making. The course covers fundamental techniques for modeling a mathematical optimization framework using data as well as solving computationally challenging optimization problems in the face of uncertainty. Topics include stochastic programming, robust optimization, inverse optimization and dynamic programming under uncertainty.

INDE7397: Simulation Modeling of Healthcare Systems (grad)

The purpose of this course is to facilitate student engagement in collaborative projects with local healthcare providers. Discrete event simulation is used for addressing various operational issues in healthcare systems.