The research from CASTLE Laboratory has contributed a substantial body of material that supports several courses that are taught by Warren Powell in the Department of Operations Research and Financial Engineering. Below are short summaries of courses currently being taught. For more information, click on the link associated with each course.

ORF 411 – Sequential Decision Analytics and Modeling

Newly re-designed, ORF 411 tackles the universal problem of making decisions over time, under different forms of uncertainty.  The course introduces a universal framework that brings together all the different tools in the “jungle of stochastic optimization.”

The course focuses on modeling, using an application-based teaching approach that uses a variety of problems to teach different modeling devices.  The goal is to then design effective policies for making decisions under uncertainty.  Students learn the four classes of policies; one of these four classes (or a hybrid) will provide an effective policy (sometimes optimal) for any problem.

ORF 411 is open to undergraduates (juniors and seniors) and graduates from any background, with just a course in probability and statistics (some python coding is required).  Students from engineering, social and physical sciences, computer science and economics are welcome.

ORF 418 – Optimal learning

This course focuses on the challenges of making good decisions that balance “learning while earning” – we want to do the best, but we have to learn while we are doing.  Applications range from the internet (choosing the best ad to maximize ad-clicks) to a variety of problems in engineering, science, and everyday life where you have to use intelligent trial-and-error.

ORF 544 – Stochastic optimization

ORF 544 is a course that teaches a unified framework for stochastic optimization and learning, spanning classical fields such as stochastic search, dynamic programming, stochastic programming, and optimal (stochastic) control, and related fields including reinforcement learning/approximate dynamic programming, model predictive control, online computation, and decision trees. We consider both offline formulations (learning in the lab) and online (learning in the field), in a simple, integrated framework. Students will see how these diverse fields can be viewed from this common framework, opening up both new problem classes, as well as new solution approaches.

The course will emphasize proper modeling, and the study of general computational frameworks rather than the highly specialized problems that yield analytical solutions. General modeling formulations will be illustrated with a wide range of applications drawn from different fields (depending on the makeup of the class), spanning business, economics, engineering, and the sciences. The course will be guided by extensive experience with computational work on a range of applications.