Decision-making processes are prevalent in many applications, yet their exact mechanism is often unknown, leading to challenges to replicate the process. For instance, how medical providers decide on treatment plans for patients or how chronic patients choose and adhere to their dietary recommendations. Much effort has been focused on learning these decisions through data-intensive approaches. However, the decision-making process is usually complex and highly constrained. While the inner workings of these constrained optimizations may not be fully known, the outcomes of them (the decisions) are often observable and available, e.g., the historical data on clinical treatments. In this talk, we focus on Inverse Optimization techniques to recover the underlying optimization models that lead to the observed decisions. Inverse optimization can be employed to infer the utility function of a decision-maker or to inform the guidelines for a complicated 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.
Speaker Biography
Kimia Ghobadi is a John C. Malone Assistant Professor of Civil and Systems Engineering, the associate director of the Center for Systems Science and Engineering (CSSE), and a member of the Malone Center for Engineering in Healthcare. She obtained her Ph.D. at the University of Toronto, and before joining Hopkins, was a postdoctoral fellow at the MIT Sloan School of Management. Her research interests include inverse optimization techniques, mathematical modeling, real-time algorithms, and analytics technics with application in healthcare systems, including healthcare operations and medical decision-making.