About

Adam Elmachtoub is an Associate Professor and Vice Chair of Industrial Engineering and Operations Research at Columbia University, and a member of the Data Science Institute and DAPLab. His research cuts across industries such as e-commerce, logistics, and energy while having two major themes: (i) designing AI methodology that integrates machine learning and decision-making, (ii) simple and fair algorithms for pricing and supply chain management. He received his Ph.D. from MIT ORC, his B.S. from Cornell ORIE, and a diploma from SnE Center at Manalapan HS. His recognitions include the INFORMS Donald P. Gaver, Jr. Early Career Award for Excellence in Operations Research, NSF CAREER Award, INFORMS Revenue Management and Pricing (RMP) Section Prize, INFORMS Junior Faculty Interest Group (JFIG) Best Paper Award, Columbia's Presidential Award for Outstanding Teaching, SOCG Great Teacher Award, two-time finalist for the INFORMS RMP Practice Award, and Forbes 30 under 30.

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

Check out some of his lectures on contextual linear optimization, contextual non-linear optimization, and fair pricing.

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

I co-founded NYC Operations Day; see the event page for 2026.

I co-organized The Artificial Intelligence School for Computer Science and Operations Research Education (AI-SCORE); see the event page for 2026.

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 Ph.D. student, please apply here. If you are interested in doing a postdoc, please contact me directly.

Yixin Liu

Jonathan Y. Tan (co-advised with Yash Kanoria)

Wenxuan Liu (co-advised with Tianyi Lin)

Haixiang Lan (co-advised with Henry Lam)

Jiaqi Shi

Hyemi Kim

Abdellah Aznag (Ph.D., co-advised with Rachel Cummings), 2026, Postdoctoral Fellow at the Harvard Business School AI Institute → Assistant Professor at University of Navarra, IESE Business School

Devansh Jalota (Postdoc, co-mentored with Sharon Di), 2026, Assistant Professor at Georgia Institute of Technology, School of Industrial and Systems Engineering

Haofeng Zhang (Ph.D., co-advised with Henry Lam), 2024, Machine Learning Researcher, Vice President at Morgan Stanley

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

Mingliu Chen (Postdoc, co-mentored with David Yao), 2023, Assistant Professor at University 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 → AI Scientist at GE Healthcare

Jacob Bergquist (Ph.D., co-advised with Karl Sigman), 2023, Quantitative Research Manager 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 → Assistant Professor at Erasmus University, Rotterdam School of Management (RSM)

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

Yunjie Sun (Ph.D.), 2019, Sr. Data Scientist at Tripadvisor → Sr. Data Scientist at ASML

Michael Hamilton (Ph.D.), 2019, Assistant Professor at University of Pittsburgh, Katz Graduate School of Business → Assistant Professor at City University of New York, Zicklin School of Business at Baruch College

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, AFOSR, and Columbia University.

Under Review

  1. Simple vs. Optimal Congestion Pricing, with Devansh Jalota and Xuan Di [code]

  2. Estimate-Then-Optimize Versus Integrated-Estimation-Optimization Versus Sample Average Approximation: A Stochastic Dominance Perspective, with Henry Lam, Haofeng Zhang, and Yunfan Zhao [code]
    • Finalist for Haofeng Zhang, INFORMS George Nicholson Student Paper Competition, 2023

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

Publications

  1. Matchmaking Strategies for Maximizing Player Engagement in Video Games, with Mingliu Chen and Xiao Lei
    Management Science, forthcoming [doi] [code]
    EC: The 23rd ACM Conference on Economics and Computation, 2022 [doi]
    • 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

  2. The Value of Flexibility from Opaque Selling, with David D. Yao and Yeqing Zhou
    Management Science, forthcoming [doi] [code]

  3. Joint Service Depth and Priority Design in Queueing Systems, with Yash Kanoria and Jonathan Y. Tan
    SIGMETRICS: International Conference on Measurement and Modeling of Computer Systems, 2027

  4. Simple Policies for Joint Pricing and Inventory Management, with Harsh Sheth and Yeqing Zhou
    WINE: The 22nd Conference on Web and Internet Economics, 2026 [code]

  5. Fair Fares for Vehicle Sharing Systems, with Hyemi Kim
    Operations Research, 2026 [doi] [code]
    FAccT: The 8th ACM Conference on Fairness, Accountability, and Transparency, 2025
    • Finalist for Hyemi Kim, INFORMS DEI Best Student Paper Award, 2024
    • Finalist for Hyemi Kim, INFORMS Transportation Science and Logistics (TSL) Society Best Student Paper Award, 2024

  6. Choice Modeling and Pricing for Scheduled Services, with Kumar Goutam and Roger Lederman
    KDD: The 32nd ACM Conference on Knowledge Discovery and Data Mining, 2026 [doi]
    • Finalist, INFORMS Innovation in Applied Analytics Award (IAAA), 2025
    • 2nd place, INFORMS Revenue Management and Pricing (RMP) Practice Award, 2024

  7. A Complexity Measure for Active Learning in Multi-group Mean Estimation, with Abdellah Aznag and Rachel Cummings
    COLT: The 39th Annual Conference on Learning Theory, 2026

  8. Fair Aggregation in Virtual Power Plants, with Liudong Chen, Hyemi Kim, and Bolun Xu
    FAccT: The 9th ACM Conference on Fairness, Accountability, and Transparency, 2026 [code]

  9. Learning Fair Demand Models, with Hyemi Kim and Jonathan Y. Tan
    FAccT: The 9th ACM Conference on Fairness, Accountability, and Transparency, 2026 [code]

  10. Static Pricing Guarantees for Queueing Systems, with Jacob Bergquist
    Stochastic Systems, 2026 [doi] [code]

  11. The Bias-Variance Tradeoff in Data-Driven Optimization: A Local Misspecification Perspective, with Haixiang Lan, Luofeng Liao, Christian Kroer, Henry Lam, and Haofeng Zhang
    NeurIPS: Neural Information Processing Systems 38, 2025 [code]

  12. Price and Assortment Optimization under the Multinomial Logit Model with Opaque Products, with Omar El Housni, Harsh Sheth, and Jiaqi Shi
    WINE: The 21st Conference on Web and Internet Economics, 2025 [doi] [code]

  13. The Power of Static Pricing for Reusable Resources, with Jiaqi Shi
    EC: The 26th ACM Conference on Economics and Computation, 2025 [doi] [code]
    • Finalist for Jiaqi Shi, INFORMS IBM Best Student Paper Award in Service Science, 2025

  14. Dissecting the Impact of Model Misspecification in Data-Driven Optimization, with Henry Lam, Haixiang Lan, and Haofeng Zhang
    AISTATS: The 28th International Conference on Artificial Intelligence and Statistics, 2025

  15. An Active Learning Framework for Multi-Group Mean Estimation, with Abdellah Aznag and Rachel Cummings
    NeurIPS: Neural Information Processing Systems 36, 2023 [doi]

  16. Generalization Bounds in the Predict-then-Optimize Framework, with Othman El Balghiti, Paul Grigas, and Ambuj Tewari
    Mathematics of Operations Research, 2023 [doi]
    NeurIPS: Neural Information Processing Systems 32, 2019

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

  18. Balanced Off-Policy Evaluation for Personalized Pricing, with Vishal Gupta and Yunfan Zhao
    AISTATS: The 26th International Conference on Artificial Intelligence and Statistics, 2023 [code]

  19. Price Discrimination with Fairness Constraints, with Maxime C. Cohen and Xiao Lei
    Management Science, 2022 [doi] [code]
    FAccT: The 4th ACM Conference on Fairness, Accountability, and Transparency, 2021 [doi]
    • INFORMS Revenue Management and Pricing (RMP) Section Prize, 2025
    • Invited to present at the UK Financial Conduct Authority (FCA), 2025
    • 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
    • Featured article, see discussion in Management Science Review

  20. Revenue Management with Product Retirement and Customer Selection, with Vineet Goyal, Roger Lederman, and Harsh Sheth
    WINE: The 18th Conference on Web and Internet Economics, 2022 [doi] [code]
    • US Patent 11151604 granted in 2021 titled "Revenue management using dynamic customer selection", with Roger Lederman

  21. 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 [doi] [animation] [code]
    EAAMO: The 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, 2021

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

  23. Smart "Predict, then Optimize", with Paul Grigas
    Management Science, 2022 [doi] [talk with Paul] [code] [PyEPO package by Bo Tang and Elias B. Khalil]
    • 1st place, INFORMS Junior Faculty Interest Group (JFIG) Paper Competition, 2020
    • Appeared in INFORMS Analytics Collections Vol. 16: Advances in Integrating AI & O.R.
    • Featured article, see discussion in Management Science Review

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

  25. Loot Box Pricing and Design, with Ningyuan Chen, Michael L. Hamilton, and Xiao Lei
    Management Science, 2021 [doi] [talk by Xiao] [code]
    EC: The 21st ACM Conference on Economics and Computation, 2020 [doi]
    • Honorable Mention for Xiao Lei (part 1 of 3), INFORMS George B. Dantzig Dissertation Award, 2023.
    • 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

  26. The Power of Opaque Products in Pricing, with Michael L. Hamilton
    Management Science, 2021 [doi] [code]
    WINE: The 13th Conference on Web and Internet Economics, 2017 [doi]
    • Featured article, see discussion in Management Science Review

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

  28. Pricing Analytics for Rotable Spare Parts, with Omar Besbes and Yunjie Sun
    INFORMS Journal on Applied Analytics, 2020 [doi] [talk]
    • Finalist, INFORMS Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research, 2019
    • Finalist (part 2 of 2), INFORMS Revenue Management and Pricing (RMP) Practice Award, 2019

  29. A Practical Method for Solving Contextual Bandit Problems Using Decision Trees, with Ryan McNellis, Sechan Oh, and Marek Petrik
    UAI: The 33rd Conference on Uncertainty in Artificial Intelligence, 2017 [code from E. Strong, B. Kleynhans, and S. Kadioglu (2021)]
    • US Patent 10546320 granted in 2020 titled "Determining feature importance and target population in the context of promotion recommendation", with Markus R. Ettl, Sechan Oh, Marek Petrik, and Rajesh K. Ravi
    • Invited for oral presentation (top 10% of submissions)

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

  31. The Submodular Joint Replenishment Problem, with Maurice Cheung, Retsef Levi, and David B. Shmoys
    Mathematical Programming, 2016 [doi]

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

  33. New Approaches for Integrating Revenue and Supply Chain Management
    Massachusetts Institute of Technology Ph.D. Thesis, 2014 [talk]

  34. 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, and William Vaughan
    UAI: The 26th Conference on Uncertainty in Artificial Intelligence, 2010
    • Invited for oral presentation (top 12% of submissions)

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

Teaching

IEOR 3700, Research Immersion in OR & Data Analytics, Spring 2026

IEOR 4418, Transportation Analytics and Logistics (B.S./M.S.), Fall 2016, Spring 2018-2023, 2025, Fall 2025

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

IEOR 4650, Business Analytics (M.S.), Spring 2016-2018, 2019 (x2), Fall 2020, Spring 2022-23, 2025, Fall 2025

IEOR 8100, Supply Chain Management (Ph.D.), Spring 2016

IEOR 8100, Contextual Optimization (Ph.D.), Fall 2019, Fall 2024