Research Overview

The Decision, Risk, & Operations division is a world leader in Analytics, Operations and Management Science. It brings together a unique set of thought leaders with broad research interests. The division's research is typically focused on the development and analysis of quantitative and empirical models of business problems. These models are used, for example, to support decision-making within organizations, to measure and manage risks, and to enhance understanding of business practices. The division's research develops and builds on state-of-the-art methodologies from stochastic analysis, mathematical programming, game theory, probability, machine learning, statistics and econometrics. While the topics and application of the research can be broad, the group has a unifying culture of rigor and relevance.

The faculty is regularly recognized through the most prestigious distinctions and awards in the field including, for example, NSF CAREER awards, INFORMS and MSOM fellowships, the Frederick W. Lanchester Prize, the Saul Gass Expository Writing Award, the INFORMS impact prize, the Erlang Prize, the Revenue Management and Pricing Section award, the MSOM young scholar prize. The division maintains strong ties with Columbia’s School of Engineering and the Data Science Institute.

DRO research areas

Faculty Research Talks

Every Spring DRO offers a Topic Seminar course where 6-7 faculty members give a lecture on their recent work in detail (3 hours each). See below for some of the recent talks:

Spring 2021

Prof. Yash Kanoria  Topics in Random Matching
Prof. Assaf Zeevi Multi-armed Bandits
Prof. Hongyao Ma LP Duality and Market Equilibrium
Prof. Ciamac Moallemi Modern Market Microstructure
Prof. Awi Federgruen Monotonicity Optimal Solutions
Prof. Daniel Russo Intellectual Core of Reinforcement Learning

Spring 2020

Prof. Awi Federgruen Competition in Multi-Echelon Systems
Prof. Santiago Balseiro Approximation for Stochastic Dynamic Programs
Prof. Paul Glasserman Topics in Markets and Information
Prof. Will Ma Topics in Online Allocation