Adam Elmachtoub is an Assistant Professor of Industrial Engineering and Operations Research at Columbia University, where he is also a member of the Data Science Institute. 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 previously received his B.S. degree from Cornell in 2009 and his Ph.D. from MIT in 2014, both in operations research. In 2014-2015, he spent one year at the IBM T.J. Watson Research Center working in the area of Smarter Commerce. In 2016, he received an IBM Faculty Award and was one of Forbes 30 under 30 in science.

For more information, please see his CV, Google Scholar page, or this article.

For media coverage, see articles in GamesIndustry, PC Gamer, Wired, and Columbia Spectator.


I have the pleasure of working with some fantastic students at Columbia. If you are interested in becoming a student, please apply here.

Ph.D. Students

Harsh Sheth (co-advised with Vineet Goyal)

Xiao Lei

Yeqing Zhou

Ryan McNellis

Yunjie Sun, 2019, Sr. Revenue Management Analyst & Data Scientist at TripAdvisor

Michael Hamilton, 2019, Assistant Professor at Katz Graduate School of Business, University of Pittsburgh


Under Review

The Value of Flexibility from Opaque Selling, with D. D. Yao and Y. Zhou

Pricing Analytics for Rotable Spare Parts, with O. Besbes and Y. Sun
Finalist, Daniel H. Wagner Prize for Excellence in Operations Research Practice, 2019.
Finalist (part 2 of 2), INFORMS Revenue Management and Pricing (RMP) Practice Award, 2019.

Pricing with Fairness, with M. C. Cohen and X. Lei

Loot Box Pricing and Design, with N. Chen, M. L. Hamilton, and X. Lei
Major revision in Management Science.
Invited to present at the Federal Trade Commission (FTC) Workshop on Consumer Issues Related to Loot Boxes, 2019 (one of four research papers selected).
1st place for Xiao Lei, IBM Service Science Best Student Paper Award, 2019.

Model Trees for Personalization, with A. Aouad, K. J. Ferreira, and R. McNellis

Static Pricing: Universal Guarantees for Reusable Resources, with O. Besbes and Y. Sun
Major revision in Operations Research.
Accepted in The 20th ACM Conference on Economics and Computation (EC), 2019.
Spotlight presentation at INFORMS Revenue Management and Pricing (RMP), 2019 (top 20% of full paper submissions).
Finalist (part 1 of 2), INFORMS Revenue Management and Pricing (RMP) Practice Award, 2019.

The Value of Personalized Pricing, with V. Gupta and M. L. Hamilton
Finalist, INFORMS Service Science Cluster Best Paper Award, 2018.
Accepted in The 15th Conference on Web and Internet Economics (WINE), 2019.

Smart "Predict, then Optimize", with P. Grigas
Major revision in Management Science.

The Power of Opaque Products in Pricing, with M. L. Hamilton
Major revision in Management Science.
Accepted in The 13th Conference on Web and Internet Economics (WINE), 2017.

Retailing with Opaque Products, with Y. Wei
Major revision in Manufacturing & Service Operations Management.


Generalization Bounds in the Predict-then-Optimize Framework, with O. El Balghiti, P. Grigas, and A. Tewari
Advances in Neural Information Processing Systems 32 (NeurIPS), 2019.

A Practical Method for Solving Contextual Bandit Problems Using Decision Trees, with R. McNellis, S. Oh, and M. Petrik
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 2017.
Invited for oral presentation (top 10% of submissions).

Supply Chain Management with Online Customer Selection, with R. Levi
Operations Research, Vol. 64(2), p. 458-473, 2016.

The Submodular Joint Replenishment Problem, with M. Cheung, R. Levi, and D. B. Shmoys
Mathematical Programming, Vol. 158(1), p. 207-233, 2016.

From Cost Sharing Mechanisms to Online Selection Problems, with R. Levi
Mathematics of Operations Research, Vol. 40(3), p. 542-557, 2015.

Maximizing the Spread of Cascades Using Network Design, with D. Sheldon, B. Dilkina, R. Finseth, A. Sabharwal, J. Conrad, C. Gomes, D. Shmoys, W. Allen, O. Amundsen, W. Vaughan
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI), p. 517-526, 2010.
Invited for oral presentation (top 12% of submissions).

From Random Polygon to Ellipse: An Eigenanalysis, with C. F. Van Loan
SIAM Review, Vol. 52(1), p. 151-170, 2010. (demo by Jason Davies)
Charles F. Van Loan selected this work as the subject for his 2018 John von Neumann Lecture.


Segmentation based estimation method for demand models under censored data, with M. R. Ettl, S. Oh, M. Petrik, and R. K. Ravi. US Patent 2018/0060885.

Revenue management using dynamic customer selection, with R. Lederman. US Patent 2017/0358001.

Training a machine to dynamically determine and communicate customized, product-dependent promotions with no or limited historical data over a network>, with M. R. Ettl, S. Oh, M. Petrik, and R. K. Ravi. US Patent 2017/0046732.

Determining feature importance and target population in the context of promotion recommendation, with M. R. Ettl, S. Oh, M. Petrik, and R. K. Ravi. US Patent 2017/0046736.


IEOR 4418, Transportation Analytics and Logistics (B.S./M.S.), Fall 2016, Spring 2018, 2019, 2020

IEOR 4574, Business Analytics (B.S./M.S.), Spring 2016, 2017 (x2), 2018 (x2), 2019 (x2), 2020

IEOR 8100, Supply Chain Management: Classics and Recent Trends (Ph.D.), Spring 2016

IEOR 8100, Prescriptive Analytics: Theory and Practice (Ph.D.), Fall 2019