Daniel Russo

 

Assistant Professor at Columbia Business School
djr2174@gsb.columbia.edu
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About Me

My research lies at the intersection of statistical machine learning and online decision making, mostly falling under the broad umbrella of reinforcement learning. I joined the Decision, Risk, and Operations division of the Columbia Business School as an assistant professor in Summer 2017. Prior to joining Columbia, I spent one great year as an assistant professor in the MEDS department at Northwestern's Kellogg School of Management and one year at Microsoft Research in New England as Postdoctoral Researcher. I recieved my PhD from Stanford University in 2015, where I was advised by Benjamin Van Roy. In 2011 I recieved my BS in Mathematics and Economics from the University of Michigan.

Selected Papers

Global Optimality Guarantees For Policy Gradient Methods, Jalaj Bhandari and Daniel Russo, working paper.

Simple Bayesian Algorithms for Best Arm Identification, Daniel Russo, Operations Research, 2020.

Controling Bias in Adaptive Data Analysis Using Information Theory, Daniel Russo and James Zou,IEEE Transaction on Information Theory, 2020

Learning to Optimize Via Information Directed Sampling, Daniel Russo and Benjamin Van Roy, Operations Research, 2018

Learning to Optimize Via Posterior Sampling, Daniel Russo and Benjamin Van Roy, Mathematics of Operations Research. 2014.