Nathan Kallus joined the faculty at Cornell in July 2016 as an Assistant Professor in the School of Operations Research and Information Engineering. His research revolves around data-driven decision making, the interplay of optimization and statistics in decision making and in inference, and the analytical capacities and challenges of observational, large-scale, and web-driven data. His works span basic theory, effective methodology, and novel applications and has been recognized by awards. Prof. Kallus 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, Prof. Kallus 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.
- 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 Research 63 (4): 868-876. .
Selected 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
- BS (Computer Science), UC Berkeley, 2009
- BA (Mathematics), UC Berkeley, 2009
- Ph D (Operations Research), Massachusetts Institute of Technology, 2015