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 CAIS, the Center for Artificial Intelligence in Society at USC. Her research is focused on Operations Research and Artificial Intelligence and in particular on optimization and machine learning. Her work is motivated by problems that are important for social good, such as those arising in public housing allocation, public health, and biodiversity conservation. 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. She serves as a member of the ad hoc INFORMS AI Strategy Advisory Committee, she is an elected member of the Committee on Stochastic Programming (COSP), and the VP of Communications for the INFORMS Section on Public Sector Operations Research. She is an Associate Editor for Operations Research Letters and Computational Management Science. She is a recipient of the NSF CAREER award and the INFORMS Diversity, Equity, and Inclusion Ambassador Program Award.
| Venue | Talk will be held via Zoom
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| Link to Register
(Via Zoom) |
https://nus-sg.zoom.us/meeting/register/tZIlduGgqz0sEtFr4NIgpQnX2vhMelYAA5b6 |
| Title | Interpretability, Robustness, and Fairness in Predictive and Prescriptive Analytics for Social Impact
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| Abstract | Motivated by problems in homeless services delivery, suicide prevention, and substance use prevention, we consider the problem of learning optimal interpretable, robust, and fair models in the form of decision-trees to assist with decision-making in socially sensitive, high-stakes settings. We propose new models and algorithms, showcase their flexibility, and theoretical and practical benefits, and demonstrate substantial improvements over the state of the art. This presentation is based on the following papers:
Strong optimal classification trees, S. Aghaei, A. Gómez, P. Vayanos. Under second round of review at Operations Research, January 2021. Learning optimal fair classification trees, N. Jo, S. Aghaei, J. Benson, A. Gómez, P. Vayanos. Under review for ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022. Learning optimal prescriptive trees from observational data, N. Jo, S. Aghaei, A. Gómez, P. Vayanos, Under Review at Management Science, August 2021. Optimal robust classification trees, N. Justin, S. Aghaei, A. Gómez, P. Vayanos, AAAI Workshop on Adversarial Machine Learning and Beyond, 2022. |
