Nathan Kallus is Assistant Professor in the School of Operations Research and Information Engineering and Cornell Tech at Cornell University. Nathan's research interests include personalization; optimization, especially under uncertainty; causal inference; sequential decision making; credible and robust inference; and algorithmic fairness. He holds a PhD in Operations Research from MIT as well as a BA in Mathematics and a BS in Computer Science both from UC Berkeley. Before coming to Cornell, Nathan was a Visiting Scholar at USC's Department of Data Sciences and Operations and a Postdoctoral Associate at MIT's Operations Research and Statistics group.
Robust optimization; Stochastic optimization; Machine learning; Causal inference; Personalization; Optimization in statistics; Data-driven decision making under uncertainty; Online decision making; Operations management and revenue management applications.
Prof. Kallus teaches Applied Machine Learning (CS 5785) and is interested in equipping future scientists and analysts with the ability to understand unstructured, observational, and large-scale data and the skills to use these data to drive effective decisions.
