Andrew and Erna Viterbi Early Career Chair
Associate Professor of Industrial & Systems Engineering and Computer Science
Co-Director, CAIS Center for Artificial Intelligence in Society
I am an Associate Professor of Industrial & Systems Engineering and Computer Science at the University of Southern California, and a Co-Director of the CAIS Center for Artificial Intelligence in Society. I hold a Viterbi Early Career Chair in Engineering and before that held a WiSE Gabilan Assistant Professorship. 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.
I am a recipient of the NSF CAREER award and the USC Viterbi Junior Research Award and, jointly with my students, earned the INFORMS Diversity, Equity, and Inclusion Ambassador Program Award. My students have earned prestigious awards, including the NSF GRFP and the USC Discovery Scholar Prize.
Since 2023, I am serving as Chair of the Committee on Stochastic Programming (COSP), the governing body of the Stochastic Programming Society. In the past, I have served as member of COSP, as member of the ad hoc INFORMS AI Strategy Advisory Committee, and as VP of Communications for the Section on Public Sector OR (PSOR) at INFORMS. I am an Associate Editor for Computational Management Science and for Operations Research Letters.
My research is supported by the National Science Foundation, by the Hilton Foundation, by the Home for Good Foundation, by the Homeless Policy Research Institute, 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.
Operations Research & Artificial Intelligence:
Robust Optimization, Integer Optimization, and Stochastic Programming and their interface with Machine Learning, Causal Inference, and Economics
Predictive and Prescriptive Analytics for High-Stakes Domains
Robustness, Interpretability, and Fairness in Machine Learning and Resource Allocation
Public Policy (housing and organ allocation)
Public Health (suicide and substance use prevention)
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 to let me know why you are interested in joining my research group.