IE Seminar: “The Surprising Power of Static Pricing in Classic OR Models”, Adam Elmachtoub, 4:00PM February 23 2024 (EN)

Speaker: Adam Elmachtoub (Columbia University, Department of Industrial Engineering and Operations Research)

Title: The Surprising Power of Static Pricing in Classic OR Models

Date: February 23, 2024 (Friday)
Time: 16:00 – 17:00
Place: Zoom

This is an online seminar. To obtain event details please send a message to department.

Title: Actionable analytics in healthcare: From intra-hospital patient transport systems to algorithmic fairness

In this talk, we survey several of our recent results on using static pricing strategies as alternatives to dynamic pricing. We focus on three fundamental settings: Erlang loss system (, M/M/1 queue (, and stochastic inventory control ( We provide strong, non-asymptotic theoretical guarantees that hold under general conditions, and provide a novel analysis that utilizes the true optimal policy as a benchmark, rather than the classic benchmark based on a deterministic upper bound. These are joint works with Jiaqi Shi, Jacob Bergquist, Harsh Sheth, and Yeqing Zhou.

Adam Elmachtoub is an Associate Professor of Industrial Engineering and Operations Research at Columbia University, where he is also a member of the Data Science Institute. He is also an Amazon Visiting Academic. His research spans two major themes: (i) designing machine learning and personalization methods to make informed decisions in industries such as retail, logistics, and travel (ii) new models and algorithms for revenue and supply chain management in modern e-commerce and service systems. He received his B.S. degree from Cornell and his Ph.D. from MIT ORC, both in operations research. He spent one year as a postdoc at the IBM T.J. Watson Research Center working in the area of Smarter Commerce. He has received an NSF CAREER Award, IBM Faculty Award, 1st place in the INFORMS JFIG (Junior Faculty) Paper Competition, Great Teacher Award from the Society of Columbia Graduates, and was on Forbes 30 under 30 in science.