Nathan Kallus

Nathan Kallus

Associate Professor
Cornell Tech
Associate Professor
Operations Research and Information Engineering


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.

Research Interests

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.

Teaching Interests

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.

Selected Publications

  • Bertsimas, D., V. Gupta, Nathan Kallus. 2016."Data-Driven Robust Optimization. " George Nicholson Student Paper Competition Finalist (INFORMS) 2013.."Mathematical Programming.
  • Bertsimas, D., V. Gupta, Nathan Kallus. 2016."Robust Sample Average Approximation." Best Student Paper (MIT Operations Research Center) 2013."Mathematical Programming (Minor revision under review).
  • Kallus, Nathan. 2016."Learning to Personalize from Observational Data. "Best Paper (INFORMS Data Mining and Decision Analytics) 2016."Operations Research.
  • Kallus, Nathan. 2014."Predicting Crowd Behavior with Big Public Data."Proceedings of the 23rd International conference on World Wide Web (WWW) companion, 23:625-630, 2014. Best Student Paper (INFORMS Social Media Analytics) 2015
  • Bertsimas, D., M. Johnson, Nathan Kallus. 2015."The Power of Optimization Over Randomization in Designing Experiments Involving Small Samples."Operations Research63(4): 868-876.

Selected Awards and Honors

  • Best Paper(INFORMS Data Mining and Decision Analytics)2016
  • Production and Operations Management Society Applied Research Challenge Finalist2016
  • Best Student Paper(INFORMS Social Media Analytics Section)2015
  • George Nicholson Student Paper Competition Finalist(INFORMS)2013
  • Best Student Paper(MIT Operations Research Center)2013


  • B.S.(Computer Science),UC Berkeley,2009
  • B.A.(Mathematics),UC Berkeley,2009
  • Ph.D.(Operations Research),Massachusetts Institute of Technology,2015


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