John R. Birge

About

John R. Birge is the Hobart W. Williams Distinguished Service Professor of Operations Management at The University of Chicago Booth School of Business. His research focuses on the mathematical modeling of systems under uncertainty, with a particular emphasis on maximizing operational and financial goals through the methodologies of stochastic programming and large-scale optimization. Birge was initially drawn to this area by a desire to apply mathematics in a useful and practical way. His work demonstrates how special problem structures can enable efficient solutions to complex decision-making problems under uncertainty. His research has received support from a variety of organizations including the National Science Foundation, Ford Motor Company, General Motors Corporation, the National Institute of Justice, the Office of Naval Research, the Electric Power Research Institute, and Volkswagen of America. Birge has published extensively and has been recognized with several prestigious awards, including the Best Paper Award from the Japan Society for Industrial and Applied Mathematics, the Institute for Operations Research and the Management Sciences Fellows Award, the Institute of Industrial Engineers Medallion Award, and election to the National Academy of Engineering.

Research topics

• Computer science
• Mathematical optimization
• Mathematics
• Business
• Economics

Selected publications

• Cluster Aware Graph Anomaly Detection

  2025-04-22 · 6 citations

  articleOpen access

  Graph anomaly detection has gained significant attention across various domains, particularly in critical applications like fraud detection in e-commerce platforms and insider threat detection in cybersecurity.Usually, these data are composed of multiple types (e.g., user information and transaction records for financial data), thus exhibiting view heterogeneity.However, in the era of big data, the heterogeneity of views and the lack of label information pose substantial challenges to traditiona…

  PublisherOA PDFDOI

• Markdown Policies for Demand Learning with Forward-Looking Customers

  Operations Research · 2025-05-08 · 5 citations

  article1st authorCorresponding

  Markdown Policies for Demand Learning with Forward-Looking Customers Demand uncertainty and forward-looking customer behavior pose substantial challenges for sellers. In “Markdown policies for demand learning with forward-looking customers,” Birge, Chen, and Keskin analyze a markdown pricing problem involving demand model uncertainty and strategic customers. The authors identify that strategic customer behavior creates a strong intertemporal dependence, where early markdowns influence later dema…

  PublisherDOI

• A Vision for Computational Decarbonization of Societal Infrastructure

  IEEE Internet Computing · 2025-03-01 · 4 citations

  article

  Modern society is at a critical inflection point with rapidly accelerating demand for energy due to growth in domestic manufacturing, datacenters, artificial intelligence (AI), electric vehicles, and electric heat pumps. Sustaining this growth while also reducing society’s carbon emissions will necessitate a shift beyond our long-standing focus on improving energy-efficiency to optimizing carbon-efficiency. This paper lays out a vision for a new field of Computational Decarbonization (CoDec), wh…

  PublisherDOI

• Learning to Schedule in Multiclass Many-Server Queues with Abandonment

  Operations Research · 2024-11-25 · 4 citations

  article

  How to Learn Which Customer Class to Serve Next? In “Learning to Schedule in Multiclass Many-Server Queues with Abandonment”, Zhong, Birge, and Ward tackle the challenge of scheduling (that is, how to choose the customer that a newly available server will serve) in a multiclass many-server queueing system where customers may abandon the queue. The goal is to develop a scheduling policy that performs nearly as well as a benchmark policy under full knowledge of the model primitives despite these p…

  PublisherDOI

• Limiting out-of-sample performance of optimal unconstrained portfolios

  Finance research letters · 2024-07-27 · 2 citations

  articleSenior author
  PublisherDOI

Frequent coauthors

• Ding‐Zhu Du

  162 shared

• M Pardalos Panos

  162 shared

• Hanif D. Sherali

  Virginia Tech

  162 shared

• Christodoulos A. Floudas

  162 shared

• M Pardalos

  University of Florida

  162 shared

• V. Jeyakumar

  64 shared

• Zden Ěk

  University of Florida

  49 shared

• Silvia Schwarze

  Universität Hamburg

  49 shared

Education

• B.S.

  Princeton University

  1977

• M.S., operations research

  Stanford University

  1979

• Ph.D., operations research

  Stanford University

  1980

Awards & honors

• Best Paper Award from the Japan Society for Industrial and A…
• Institute for Operations Research and the Management Science…
• Institute of Industrial Engineers Medallion Award
• elected to the National Academy of Engineering

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