Kimia Ghobadi – Inverse Optimization

Abstract: Many applications utilize optimization models, but the correct parameters for the optimization models are usually hard to know. While the process of optimal decision-making may be unknown, there are often solutions (or observations) of the system that are available. In this talk, we focus on Inverse Optimization techniques to infer the parameters of optimization models from a set of observations. Inverse optimization can be employed to infer the utility function of a decision-maker or to inform the guidelines for a complex process. We present a data-driven inverse linear optimization framework (called Inverse Learning) that finds the optimal solution to the forward problem based on the observed data. We discuss how combining inverse optimization with machine learning techniques can utilize the strengths of both approaches. Finally, we validate the methods using examples in the context of precision nutrition and personalized daily diet recommendations.

Bio: Kimia Ghobadi is a John C. Malone Assistant Professor of Civil and Systems Engineering and a member of the Malone Center for Engineering in Healthcare, the Center for Systems Science and Engineering (CSSE), and the Center for Data Science in Emergency Medicine. Prior to joining JHU, she was a postdoctoral fellow at MIT Sloan School of Management and obtained her PhD from the University of Toronto in Industrial Engineering. Her research interests are in developing inverse and forward optimization models, real-time algorithms, and analytics technics with application in healthcare systems including healthcare operations and medical decision-making.