Official course description: Stochastic programming and robust optimization are optimization tools dealing with a class of models and algorithms in which data is affected by uncertainty, i.e., some of the input data are not perfectly known at the time the decisions are made. Topics include modeling uncertainty in optimization problems, two-stage and multistage stochastic programs with recourse, chance constrained programs, computational solution methods, approximation and sampling methods, and applications. Knowledge of linear programming, probability and statistics are required, while programming ability and knowledge of integer programming are helpful.