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Nathan Kallus is an Associate Professor at the School of Operations Research and Information Engineering and Cornell Tech at Cornell University. His research interests encompass personalization, optimization under uncertainty, causal inference, and sequential decision making, with a focus on credible robust inference and algorithmic fairness. Before joining Cornell, he served as a Visiting Scholar at the University of Southern California’s Department of Data Sciences and Operations and as a Postdoctoral Associate at MIT’s Operations Research Statistics group. Nathan earned his PhD in Operations Research from the Massachusetts Institute of Technology and holds Bachelor's degrees in Mathematics and Computer Science from the University of California, Berkeley. His teaching interests include Applied Machine Learning, where he aims to equip future scientists and analysts with skills to understand unstructured, observational, large-scale data to drive effective decisions.
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