In the first part of the talk, we present a data-driven optimization approach to estimate wait times for individual patients in the U.S. Kidney Allocation System, based on the very limited system information that they possess in practice. To deal with this information incompleteness, we develop a novel robust optimization analytical framework for wait time estimation in multiclass, multiserver queuing systems. We calibrate our model with highly detailed historical data and illustrate how it can be used to inform medical decision making and improve patient welfare.

In the second part of the talk, we present a data-driven optimization approach for designing fair, efficient, and interpretable policies for prioritizing heterogeneous homeless youth on a waiting list for scarce housing resources. Our framework provides the policy-maker the flexibility to select both their desired structure for the policy and their desired fairness requirements. We evaluate our framework using real-world data from the United States homeless youth housing system. We show that our framework results in policies that are more fair than the current policy in place and than classical interpretable machine learning approaches while achieving a similar (or higher) level of overall efficiency.

The first part of the talk is joint work with Chaitanya Bandi and Nikolaos Trichakis and is forthcoming in Management Science. The second part of the talk is joint work with Mohammad Javad Azizi, Bryan Wilder, Eric Rice, and Milind Tambe and is forthcoming in the Proceedings of the 15th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2018.

—

Phebe Vayanos is an Assistant Professor of Industrial & Systems Engineering and Computer Science at the University of Southern California. She is also an Associate Director of the CAIS Center for Artificial Intelligence in Society at USC. Her research aims to address fundamental questions arising in data-driven integer and robust optimization, and game theory. Her work is motivated by decision-making and resource allocation problems that are important for social good, such as those arising in public health, public safety and security, biodiversity preservation, education, and energy. Prior to joining USC, she was lecturer in the Operations Research and Statistics Group at the MIT Sloan School of Management, and a postdoctoral research associate in the Operations Research Center at MIT. She holds a PhD degree in Operations Research and an MEng degree in Electrical & Electronic Engineering, both from Imperial College London.

Faculty Host: Fei Fang