Bora Keskin

Bora Keskin is an Associate Professor in the Operations Management area at the Fuqua School of Business at Duke University. Bora received his Ph.D. from the Graduate School of Business at Stanford University in 2012. Before joining the faculty at Duke University in 2015, he worked at McKinsey & Company as a consultant in banking and telecommunications industries, and at the University of Chicago as an Assistant Professor of Operations Management.

Bora's main research studies management problems that involve decision making under uncertainty. In particular he is interested in stochastic models and their application to revenue management, dynamic pricing, statistical learning, machine learning, and product differentiation. Bora has published papers in leading research journals such as Management Science, Operations Research, Manufacturing and Service Operations Management, and Mathematics of Operations Research. In 2019, Bora was awarded the Lanchester Prize for the development of a novel paradigm for the modeling and analysis of online dynamic optimization problems that are subject to temporal uncertainty.

Bora has taught Value Chain Innovation in Business Processes as well as Supply Chain Management for the Daytime and Executive MBA programs, and Revenue Management for the PhD program at the Fuqua School of Business. Outside Duke, he served as a Board Member for the INFORMS Revenue Management and Pricing (RM&P) Section from 2014-2016, and as a Cluster Chair for the RM&P and M&SOM-Service tracks at INFORMS Annual Meetings (organizing 319 talks in total).

Teaching / Research Interests

Dynamic pricing, revenue management, statistical learning, machine learning, exploration-exploitation tradeoff, information asymmetry, product differentiation, applied probability

Education

• Ph.D. Stanford University Graduate School of Business (2007 — 2012)
• B.Sc. Boğaziçi Üniversitesi (2001 — 2007)

Selected Honors and Awards

• Winner, Lanchester Prize, 2019
• Winner, INFORMS Data Mining Best Paper Competition, 2020
• Finalist, INFORMS Data Mining Best Paper Competition, 2019
• Honorable Mention, INFORMS Junior Faculty Interest Group (JFIG) Paper Competition, 2018
• STAR Visitor, Netherlands Organization for Scientific Research, 2015

Publications

• Bayesian Dynamic Pricing Policies: Learning and Earning under a Binary Prior Distribution, Management Science, Vol. 58, No. 3, March 2012, pp. 570-586, with J.M. Harrison and A. Zeevi.
• Dynamic Pricing with an Unknown Demand Model: Asymptotically Optimal Semi-myopic Policies, Operations Research, Vol. 62, No. 5, September-October 2014, pp.
• Chasing Demand: Learning and Earning in a Changing Environment, Mathematics of Operations Research, Vol. 42, No. 2, May 2017, pp. 277-307, with A. Zeevi.
  — Lead Article.
  — Winner, Lanchester Prize, 2019.
• On Incomplete Learning and Certainty-Equivalence Control, Operations Research, Dynamic Selling Mechanisms for Product Differentiation and Learning, Operations Research, Vol. 67, No. 4, July-August 2019, pp. 1069-1089, with J. Birge.
• Discontinuous Demand Functions: Estimation and Pricing, Management Science, Personalized Dynamic Pricing with Machine Learning: High Dimensional Features and Heterogeneous Elasticity, Management Science, Vol. 67, No. 9, September 2021, pp. 5549-5568, with G.-Y. Ban.
  — Honorable Mention, INFORMS Junior Faculty Interest Group (JFIG) Paper Competition, 2018.
  — Finalist, INFORMS Data Mining Best Paper Competition, 2019.
• Competition between Two-Sided Platforms under Demand and Supply Congestion Effects, forthcoming, M&SOM, with F. Bernstein and G. DeCroix.
• Impact of Information Asymmetry and Limited Production Capacity on Business Interruption Insurance, forthcoming, Management Science, with Y.-M. Kao and K. Shang.
• Dynamic Learning and Market Making in Spread Betting Markets with Informed Bettors, forthcoming, Operations Research, with J. Birge, Y. Feng, and A. Schultz.
  — Preliminary Version in the Proceedings of the 2019 ACM Conference on Economics and Computation (EC '19).
  — Featured in Chicago Booth Review.
• Data-driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment, forthcoming, Management Science, with Y. Li and J. Song.
• Dynamic Pricing with Demand Learning and Reference Effects, forthcoming, Management Science, with A. den Boer.