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.
- Data Mining
- Complex Systems, Network Science and Computation
- Data Science
- Statistics and Machine Learning
- Information Technology Modeling
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.
- 2016."Data-Driven Robust Optimization. " George Nicholson Student Paper Competition Finalist (INFORMS) 2013.."Mathematical Programming. .
- 2016."Robust Sample Average Approximation." Best Student Paper (MIT Operations Research Center) 2013."Mathematical Programming (Minor revision under review). .
- 2016."Learning to Personalize from Observational Data. "Best Paper (INFORMS Data Mining and Decision Analytics) 2016."Operations Research. .
- 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 .
- 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