Phebe Vayanos

Assistant Professor of Industrial & Systems Engineering and Computer Science

Associate Director, CAIS Center for Artificial Intelligence in Society

Viterbi School of Engineering

University of Southern California

I am Assistant Professor of Industrial & Systems Engineering and Computer Science at the University of Southern California, and Associate Director of the CAIS Center for Artificial Intelligence in Society. Prior to joining USC, I was a 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. I hold a PhD degree in Operations Research and an MEng degree in Electrical & Electronic Engineering, both from Imperial College London.

My research aims to address fundamental questions in data-driven optimization (a.k.a. prescriptive analytics) with aim to tackle real-world decision- and policy-making problems in uncertain and adversarial environments. My work is motivated by resource allocation problems that are important for social good, such as those arising in public health, public safety and security, public housing, biodiversity preservation, and education. I am also interested in issues surrounding fairness, efficiency, and interpretability in resource allocation. My aim is to advance research in Operations Research and Artificial Intelligence in a manner that will benefit society and in particular low resource communities and others that have not benefited from these recent developments.

My research is supported by the NSF, by the U.S. Army Research Laboratory's Army Research Office, by Schmidt Futures, by the METRANS Transportation Center, and by the Zumberge Diversity & Inclusion Grant Program at USC, among others.

My Google Scholar profile can be viewed here.

Contact Information:

Email: phebe.vayanos@usc.edu

Office: Olin Hall of Engineering (OHE) 310L, University Park Campus, USC

Research Interests:

Artificial Intelligence and Operations Research: Data-Driven Optimization; Prescriptive Analytics; Decision-Making under Uncertainty; Robust Optimization; Fairness, Efficiency, Transparency in Machine Learning and Resource Allocation; Game Theory; Applications in: Public Health, Public Safety and Security, Public Housing, Biodiversity Preservation, etc.

Prospective Students:

I am always looking for motivated Undergraduate, Master's, and PhD students that are interested in both theory and applications. If you are interested in becoming a student, please apply here. You can also send me an email detailing why you are interested in joining my research group.