Operations, Information & Technology

In the Operations, Information, and Technology field we use mathematical models to improve technological systems.

We develop new methods, improve the use of emerging technologies, study a wide variety of systems, and impact practice, using tools from operations research, game theory, econometrics, computer science, probability and statistics.

Our faculty research interests include health care systems, product design and manufacturing processes, supply networks, information systems, energy and environmental systems, homeland security systems, financial systems, social networks, and online markets. Our faculty-student ratio is approximately one-to-one allowing for personalized attention to students. 

Preparation and Qualifications

The program is intended for students with strong training in relevant mathematical methods and models who are interested in academic careers. Students who enroll in this program must have strong preparation in advanced calculus, linear algebra, or probability. Competence in optimization, computer programming, microeconomics, and classical statistics is also helpful. Recent admits have majored in Electrical and Industrial Engineering, Math, Statistics and Economics.

Recent Journal Articles in Operations, Information & Technology

Santiago Balseiro, Yonatan Gur
Management Science. September
2019, Vol. 65, Issue 9, Pages 3952-3968
Joann de Zegher, Dan A. Iancu, Hau L. Lee
Manufacturing and Service Operations Management. June
2019, Vol. 21, Issue 2, Pages 251-477
Kostas Bimpikis, Davide Crapis, Alireza Tahbaz-Salehi
Management Science. June
2019, Vol. 65, Issue 6, Pages 2646-2664

Recent Insights by Stanford Business

December 6, 2019
Communication mistakes, job-stealing robots, and career-gap explanations captured reader attention this year.
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December 6, 2019
Stanford GSB professors recommend the books they’re most likely to give as presents over this year’s holiday season.
A stack of books being unwrapped. Credit: iStock/Thinglass
October 28, 2019
Credit-market algorithms may violate anti-discrimination laws even when they’re designed not to. A Stanford researcher looks for a fix.
A young black man, anxious about his bills. Credit: iStock/Damir Khabirov