Robert Bray
Robert Bray is an associate professor of operations management. He received his B.S. in industrial engineering, operations research from UC Berkeley in 2006, and his Ph.D. in business administration from the Stanford Graduate School of Business in 2012. His research focuses on supply chain management, dynamic programming, and empirical operations management. He grew up in Palos Verdes, CA.
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PhD, 2012, Operations, Information, and Technology, Stanford Graduate School of Business, Stanford University
BS, 2006, Industrial Engineering Operations Research, University of California, Berkeley -
Donald P. Jacobs Scholar/Assistant Professor, Managerial Economics and Decision Sciences, Kellogg School of Management, Northwestern University, 2012-present
Lecturer: Introduction to Operations (PhD), Stanford Graduate School of Business, Stanford University, 2009-2011
Course Assistant: Electronic Commerce (MBA), Stanford Graduate School of Business, Stanford University, 2009-2011
Teaching Assistant: Discrete Event Simulation (undergraduate), University of California at Berkeley, 2006
Research Assistant: Cohn Visual Detection Laboratory, University of California at Berkeley, 2004-2005 -
Referee: Management Science, Manufacturing and Service Operations Management, 2009-present
Participant: Chicago-Argonne Initiative for Computational Economics, Program on estimation of structural econometric models, 2009
Student consulting project, Restoration Hardware, 2006
Semiconductor Manufacturing Consultant, Leachman & Associates, 2006-2007
Manufacturing Engineering Co-op, Intel, 2005
Industrial Engineering Co-op, Disneyland, 2005
Participant: Georgia Tech East Asia Industrial Engineering Program, Tsinghua University and National University of Singapore, 2004 -
Manufacturing and Service Operations Management Meritorious Service Award, Manufacturing and Service Operations Management Society, 2019
Distinguished Service Award, Management Science
Distinguished Service Award, Management Science
Distinguished Service Award, Management Science
Distinguished Service Award, Management Science, 2017
Distinguished Service Award, Management Science, 2019
Finalist, Manufacturing & Service Operations Management Best Paper Award, Manufacturing and Service Operations Management, 2015-2018
Winner, Data Driven Research Challenge, Manufacturing & Service Operations Management, 2018
Manufacturing and Service Operations Management Meritorious Service Award, Manufacturing & Service Operations Management, 2017
Outstanding Reviewer Recognition, Production and Operations Management Society (POMS), 2016
Finalist, Manufacturing & Service Operations Management Best Paper Award, Manufacturing & Service Operations Management
Finalist, Management Science Best Paper in Operations Management Award, INFORMS, 2011-2014
Finalist, Management Science Best Paper in Operations Management Award, INFORMS, 2010-2013
Winner, Best Student Paper Competition, Production and Operations Management, 2011
Finalist, George Nicholson Student Paper Competition, INFORMS, 2011
Finalist, MSOM Student Paper Competition, MSOM Society, 2011 -
Associate Editor, Operations Research, 2024
Associate Editor, Manufacturing & Service Operations Management (M&SOM), 2024
Associate Editor, Management Science, 2020
Estimation of Dynamic Programs (OPNS-523-0)
This seminar will cover methods for estimating empirical dynamic discrete choice models. We will put the econometric theory to practice with weekly computer lab sessions and several rigorous programming assignments. We will study applications from the operations management area, including inventory control, supply chain coordination, service operations, and facility positioning.
Data Science with Large Language Models (OPNS-451-0)
This class will teach you how to analyze data with Large Language Models (LLMs). LLMs such as ChatGPT are powerful. To maximize your productivity---and stay relevant---you should aim to delegate as much of your workflow to these language engines as possible. This means you should switch from analyzing data with a spreadsheet to analyzing data with a computer language---such as R---which LLMs excel at reading and writing. This class will prompt you to make the switch. You will use ChatGPT to create R programs that manipulate and analyze large operational datasets. You will perform in-depth analyses of Alibaba's deliveries, the Italian judiciary's case scheduling, a Chinese supermarket's supply chain, the auto industry's supplier network, Wine Spectator's reviews, and safety breaches at nuclear power plants. You will use an R textbook the instructor wrote specifically for this class (no previous R experience is assumed). Finally, 100% of the class will be "open ChatGPT."
Applied Advanced Analytics (OPNS-441-0)
In this course, we will learn how to transform and study large operational datasets with the R programming language. R is tailor-made for analyzing data: we will consider the problem of data analysis from a design perspective, and discuss the features and tradeoffs that make R particularly good at manipulating data. We will use R to study proprietary data relating to Alibaba's deliveries, the Italian judiciary's case scheduling, a Chinese supermarket's supply chain, the auto industry's supplier network, Wine Spectator's reviews, and safety breaches at American nuclear power plants. This course is reserved for MMM students only.
