Dr. Nathan Kallus

Cornell Tech • New York, NY

Nathan Kallus is an Associate Professor at Cornell Tech, focusing on operations research and information engineering. His research interests include causal inference, particularly where machine learning and statistics intersect, as well as optimization under uncertainty and sequential dynamic decision-making. Kallus is also concerned with algorithmic fairness. He authored a book titled 'Applied Causal Inference Powered by ML AI'.

University of Southern California • Los Angeles, CA

Before joining Cornell, Kallus was a visiting scholar at the University of Southern California's Department of Data Sciences and Operations.

MIT’s Operations Research Statistics Group • Cambridge, MA

Kallus served as a postdoctoral associate in MIT's Operations Research Statistics group.