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Nathan Kallus

Associate Professor

School of Operations Research and Information Engineering

Nathan Kallus
Nathan Kallus
Graduate Field Affiliations
Applied Information Systems
Applied Mathematics
Computational Science and Engineering (minor)
Computer Science
Data Science (minor)
Economics
Operations Research and Information Engineering
Statistics

Biography

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.

  • Data Mining
  • Complex Systems, Network Science and Computation
  • Data Science
  • Statistics and Machine Learning
  • Information Technology Modeling
  • Optimization

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.

Select 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.

Select Awards and Honors

  • Best Paper (INFORMS Data Mining and Decision Analytics) 2016
  • Production and Operations Management Society Applied Research Challenge Finalist 2016
  • 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

Education

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