About

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, both in operations research. He spent one year as a postdoctoral researcher at the IBM T.J. Watson Research Center, working in the department of Business Analytics and Mathematical Sciences. 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.

For more information, please see his CV, dblp, and Google Scholar.

For media coverage, see articles in The Atlantic, CNN, U.S News, NewsWise, ConsumerAffairs, GamesIndustry, PC Gamer, Wired, and Columbia Spectator.

Team

I have the pleasure of working with some fantastic Ph.D students and postdoctoral researchers at Columbia. If you are interested in becoming a student, please apply here. If you are interested in doing a postdoc, please contact me directly.

Haixiang Lan (co-advised with Henry Lam)

Jiaqi Shi

Hyemi Kim

Abdellah Aznag (co-advised with Rachel Cummings)

Haofeng Zhang (co-advised with Henry Lam)

Harsh Sheth (Ph.D., co-advised with Vineet Goyal), 2024, Quantitative Researcher at Susquehanna International Group (SIG)

Mingliu Chen (Postdoc, co-advised with David Yao), 2023, Assistant Professor at Univerity of Texas at Dallas, Naveen Jindal School of Management

Yunfan Zhao (Ph.D.), 2023, Postdoctoral Fellow at the Harvard Center for Research on Computation and Society

Jacob Bergquist (Ph.D., co-advised with Karl Sigman), 2023, Quantitative Researcher at Andreessen Horowitz (a16z)

Xiao Lei (Ph.D.), 2022, Assistant Professor at University of Hong Kong, HKU Business School

Yeqing Zhou (Ph.D.), 2021, Assistant Professor at Eindhoven University of Technology (TU/e), School of Industrial Engineering & Innovation Sciences

Ryan McNellis (Ph.D.), 2020, Applied Research Scientist at Amazon

Yunjie Sun (Ph.D.), 2019, Senior Data Scientist at Tripadvisor, now at ASML

Michael Hamilton (Ph.D.), 2019, Assistant Professor at University of Pittsburgh, Katz Graduate School of Business

Research

A lot of this research has been generously funded by the National Science Foundation [CMMI-1763000, CMMI-1944428, IIS-2147361], Dassault Falcon Jet, IBM, and Columbia University.

Under Review

  1. Fair Fares for Vehicle Sharing Systems, with Hyemi Kim [code]

  2. Simple Policies for Joint Pricing and Inventory Management, with Harsh Sheth and Yeqing Zhou [code]

  3. Static Pricing Guarantees for Queueing Systems, with Jacob Bergquist [code]

  4. Estimate-Then-Optimize Versus Integrated-Estimation-Optimization Versus Sample Average Approximation: A Stochastic Dominance Perspective, with Henry Lam, Haofeng Zhang, and Yunfan Zhao [code]
    Accepted to INFORMS Optimization Society (IOS), 2024
    Finalist for Haofeng Zhang, INFORMS George Nicholson Student Paper Competition, 2023

  5. The Power of Static Pricing for Reusable Resources, with Jiaqi Shi [code]

  6. Matchmaking Strategies for Maximizing Player Engagement in Video Games, with Mingliu Chen and Xiao Lei
    Honorable Mention for Xiao Lei (part 2 of 3), INFORMS George B. Dantzig Dissertation Award, 2023.
    3rd place for Xiao Lei, INFORMS IBM Best Student Paper Award in Service Science, 2022
    Accepted to The 23rd ACM Conference on Economics and Computation (EC), 2022

  7. The Value of Flexibility from Opaque Selling, with David D. Yao and Yeqing Zhou [code]

  8. Retailing with Opaque Products, with Yehua Wei and Yeqing Zhou [code]

Publications

  1. An active learning framework for multi-group mean estimation, with Abdellah Aznag and Rachel Cummings
    Neural Information Processing Systems 37 (NeurIPS), 2023

  2. Generalization Bounds in the Predict-then-Optimize Framework, with Othman El Balghiti, Paul Grigas, and Ambuj Tewari
    Mathematics of Operations Research, 2023
    Accepted to Neural Information Processing Systems 33 (NeurIPS), 2019

  3. Market Segmentation Trees, with Ali Aouad, Kris J. Ferreira, and Ryan McNellis
    Manufacturing & Service Operations Management, 2023 [code]

  4. Balanced Off-Policy Evaluation for Personalized Pricing, with Vishal Gupta and Yunfan Zhao
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2023 [code]

  5. Price Discrimination with Fairness Constraints, with Maxime C. Cohen and Xiao Lei
    Management Science, 2022 [code]
    Honorable Mention for Xiao Lei (part 3 of 3), INFORMS George B. Dantzig Dissertation Award, 2023.
    Finalist for Xiao Lei, INFORMS Revenue Management and Pricing (RMP) Jeff McGill Student Paper Award, 2022
    Accepted to The 4th ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2021
    Featured article, see discussion in Management Science Review

  6. Revenue Management with Product Retirement and Customer Selection, with Vineet Goyal, Roger Lederman, and Harsh Sheth
    Proceedings of The 18th Conference on Web and Internet Economics (WINE), 2022 [code]

  7. Queuing Safely for Elevator Systems amidst a Pandemic, with Sai Mali Ananthanarayanan, Charles C. Branas, Clifford Stein, and Yeqing Zhou
    Production and Operations Management, 2022 [animation] [code]
    Accepted to The 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), 2021

  8. Static Pricing: Universal Guarantees for Reusable Resources, technical note, with Omar Besbes and Yunjie Sun
    Operations Research, 2022 [talk] [code]
    Accepted to The 20th ACM Conference on Economics and Computation (EC), 2019
    Finalist (part 1 of 2), INFORMS Revenue Management and Pricing (RMP) Practice Award, 2019

  9. Smart "Predict, then Optimize", with Paul Grigas
    Management Science, 2022 [talk with Paul] [code] [PyEPO package by Bo Tang and Elias B. Khalil]
    1st place, INFORMS Junior Faculty Interest Group (JFIG) Paper Competition, 2020
    Featured article, see discussion in Management Science Review

  10. The Value of Personalized Pricing, with Vishal Gupta and Michael L. Hamilton
    Management Science, 2021 [code]
    Finalist, INFORMS Best Cluster Paper Award in Service Science, 2018
    Accepted to The 15th Conference on Web and Internet Economics (WINE), 2019

  11. Loot Box Pricing and Design, with Ningyuan Chen, Michael L. Hamilton, and Xiao Lei
    Management Science, 2021 [talk by Xiao] [code]
    Honorable Mention for Xiao Lei (part 1 of 3), INFORMS George B. Dantzig Dissertation Award, 2023.
    Accepted to The 21st ACM Conference on Economics and Computation (EC), 2020
    Invited to present at the Federal Trade Commission (FTC), 2019 [report] [poster]
    1st place for Xiao Lei, INFORMS IBM Best Student Paper Award in Service Science, 2019

  12. The Power of Opaque Products in Pricing, with Michael L. Hamilton
    Management Science, 2021 [code]
    Accepted to The 13th Conference on Web and Internet Economics (WINE), 2017
    Featured article, see discussion in Management Science Review

  13. Decision Trees for Decision-Making under the Predict-then-Optimize Framework, with Jason C. N. Liang and Ryan McNellis
    Proceedings of the 37th International Conference on Machine Learning (ICML), 2020 [code]

  14. Pricing Analytics for Rotable Spare Parts, with Omar Besbes and Yunjie Sun
    INFORMS Journal on Applied Analytics, 2020 [talk]
    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

  15. A Practical Method for Solving Contextual Bandit Problems Using Decision Trees, with Ryan McNellis, Sechan Oh, and Marek Petrik
    Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 2017 [code from E. Strong, B. Kleynhans, and S. Kadioglu (2021)]
    Invited for oral presentation (top 10% of submissions)

  16. Supply Chain Management with Online Customer Selection, with Retsef Levi
    Operations Research, 2016 [code]

  17. The Submodular Joint Replenishment Problem, with Maurice Cheung, Retsef Levi, and David B. Shmoys
    Mathematical Programming, 2016

  18. From Cost Sharing Mechanisms to Online Selection Problems, with Retsef Levi
    Mathematics of Operations Research, 2015
    INFORMS President's Pick for October 2015

  19. New Approached for Integrating Revenue and Supply Chain Management
    Massachusetts Institute of Technology Ph.D. Thesis, 2014

  20. Maximizing the Spread of Cascades Using Network Design, with Daniel Sheldon, Bistra Dilkina, Ryan Finseth, Ashish Sabharwal, Jon Conrad, Carla Gomes, David Shmoys, William Allen, Ole Amundsen, William Vaughan
    Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI), 2010
    Invited for oral presentation (top 12% of submissions)

  21. From Random Polygon to Ellipse: An Eigenanalysis, with Charles F. Van Loan
    SIAM Review, 2010 [demo by Jason Davies]
    Charles F. Van Loan selected this work as the subject for his 2018 John von Neumann Lecture

Patents

Revenue management using dynamic customer selection, with Roger Lederman. US Patent 11151604, 2021 (granted).

Determining feature importance and target population in the context of promotion recommendation, with Markus R. Ettl, Sechan Oh, Marek Petrik, and Rajesh K. Ravi. US Patent 10546320, 2020 (granted).

Teaching

IEOR 4418, Transportation Analytics and Logistics (B.S./M.S.), Fall 2016, Spring 2018-2023

IEOR 4650, Business Analytics (B.S.), Spring 2016-2018, 2021, 2023

IEOR 4650, Business Analytics (M.S.), Spring 2016-2018, 2019 (x2), 2022-23, Fall 2020

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

IEOR 8100, Contextual Optimization for Prescriptive Analytics (Ph.D.), Fall 2019