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Nathan Kallus is an Associate Professor in Operations Research and Information Engineering at Cornell Tech. His research interests encompass causal inference, particularly in conjunction with machine learning; statistics; optimization under uncertainty; sequential dynamic decision making; and algorithmic fairness. He is the author of the book "Applied Causal Inference Powered by Machine Learning and Artificial Intelligence". Kallus earned his Ph.D. in operations research from the Massachusetts Institute of Technology and holds a B.A. in mathematics and a B.S. in computer science from the University of California, Berkeley. Before joining Cornell, Kallus was a visiting scholar at the University of Southern California's Department of Data Sciences and Operations and served as a postdoctoral associate in MIT's Operations Research and Statistics group.
Cornell Tech • New York
Teaching and conducting research in Operations Research and Information Engineering.
The M.Eng. in ORIE at Cornell Tech focuses on Data Analytics and Operations Research in a tech environment.