Daniel Russo

Dan joined the Decision, Risk, and Operations division of the Columbia Business School in Summer 2017. He teaches a core MBA course on statistics and a PhD course on dynamic optimization. His research lies at the intersection of statistical machine learning and online decision making, mostly falling under the broad umbrella of reinforcement learning. Outside academia, he works with Spotify to apply reinforcement learning style models to audio recommendations.

Dan’s research has been recognized by the Frederick W. Lanchester Prize, a Junior Faculty Interest Group Best Paper Award, and first place in the George Nicholson Student Paper Competition. He serves as an associate editor at the journals Management Science and Stochastic Systems.

Prior to joining Columbia, he spent one 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. He received his PhD from Stanford University in 2015, advised by Benjamin Van Roy. He received his BS in Mathematics and Economics from the University of Michigan in 2011.