Dr. Gah-Yi Ban is an Associate Professor of Analytics & Operations at Imperial College Business School and an Amazon Visiting Academic. Prior to joining Imperial, Gah-Yi was a faculty at the Robert H. Smith Business School, University of Maryland and London Business School, and a visiting faculty at Columbia Business School. She obtained her PhD from the University of California, Berkeley in Industrial Engineering & Operations Research.
Gah-Yi's research is in Big Data analytics; specifically, decision-making with complex, high-dimensional and highly uncertain data with business applications. Gah-Yi's research has been honored with the 2021 Best OM Paper in Operations Research award, 2019 INFORMS Data Mining Section Best Paper Award (finalist) and 2018 INFORMS JFIG Paper Competition (Honorable Mention). Gah-Yi is currently an associate editor of Management Science, M&SOM and Operations Research Letters, a member of the World Economic Forum Expert Network, and has served as Chair of Supply Chain SIG of M&SOM society and associate editor of Service Science. Gah-Yi has taught across MIM, FT and PT MBA, Executive MBA and PhD programs and has been honored with a Best Teacher Award, named one of Poets & Quants World’s Best 40 Under 40 MBA Professors and Britannica's 20 Under 40 Young Shapers of the Future (Education). She has also delivered a TEDx talk, "The Power and Perils of Algorithms".
Gah-Yi was born in Seoul, South Korea, spent her formative years in Sydney, Australia, and has since lived and worked in the U.S. and the U.K. In her spare time she enjoys running and taking her baby to museums.
Ban G-Y, Keskin NB, 2021, Personalized dynamic pricing with machine learning: high-dimensional features and heterogeneous elasticity, Management Science, Vol:67, ISSN:0025-1909, Pages:5549-5568
Ban G-Y, 2020, Confidence intervals for data-driven inventory policies with demand censoring, Operations Research, Vol:68, ISSN:0030-364X, Pages:309-326
Ban G-Y, Gallien J, Mersereau AJ, 2019, Dynamic procurement of new products with covariate information: the residual tree method, Manufacturing & Service Operations Management, Vol:21, ISSN:1526-5498, Pages:798-815
Ban G-Y, Rudin C, 2019, The big data newsvendor: practical insights from machine learning, Operations Research, Vol:67, ISSN:0030-364X, Pages:90-108