Daniel Russo is an assistant professor in the Decision, Risk, and Operations division of the Columbia Business School. His research lies at the intersection of statistical machine learning and sequential decision-making, and contributes to the fields of online optimization, reinforcement learning, and sequential design of experiments. He joined after spending a year as an assistant professor 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, under the supervision of Benjamin Van Roy. In 2011, he received his BS in Mathematics and Economics from the University of Michigan.
- Theory of Reinforcement Learning, Fall 2020. Visiting Scientist.