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IEOR Seminar Series: Yingfei Wang, University of Washington
IEOR seminars occur on Mondays throughout the fall semester in room 3108 of Etcheverry Hall. Seminars feature leading-edge research from experts in industrial engineering and operations research who come from local, national, and international institutions. Seminars are open to students, faculty, and the public.
Title: Smart Testing with Vaccination: A Bandit Algorithm for Active Sampling for Managing COVID-19
Abstract:This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers. The smart-testing ideas presented here are motivated by active learning and multi-armed bandit techniques in machine learning. Our active sampling method works in conjunction with quarantine policies, can handle different objectives, and is dynamic and adaptive in the sense that it continually adapts to changes in real-time data. The bandit algorithm uses contact tracing, location-based sampling and random sampling in order to select specific individuals to test. Using a data-driven agent-based model simulating New York City we show that the algorithm samples individuals to test in a manner that rapidly traces infected individuals. Experiments also suggest that smart-testing can significantly reduce the death rates as compared to current methods, with or without vaccination. While smart testing strategies can help mitigate disease spread, there could be unintended consequences with fairness or bias when deployed in real-world settings. To this end we show how procedural fairness can be incorporated into our method and present results that show that this can be done without hurting the effectiveness of the mitigation that can be achieved.
Bio: Yingfei Wang is an Assistant Professor at Department of Information Systems and Operations Management, University of Washington. Her research lies at the intersection of data analytics, statistics, machine learning and management information systems, exploring the ways where efficient information collection influences and improves decision-making strategies. She is devoted to solving machine learning challenges to provide efficient business solutions, using technologies from deep learning, multi-armed bandits, Bayesian optimization, and beyond. Her work has been published in Management Science, Information Systems Research, Journal on Computing, Siam Journals, IEEE Transactions on Knowledge and Data Engineering, and leading CS conferences. Yingfei holds a Ph.D. degree from Department of Computer Science at Princeton University, and a BS degree in Computer Science from Peking University.