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 Ph.D. from MIT ORC, his B.S. degree from Cornell ORIE, and secondary education from the Science/Engineering Center at Manalapan HS. He has received the Donald P. Gaver, Jr. Early Career Award, NSF CAREER Award, IBM Faculty Award, INFORMS JFIG (Junior Faculty) Paper Award, Great Teacher Award from the Society of Columbia Graduates, twice finalist for INFORMS Revenue Management and Pricing Practice Award, and Forbes 30 under 30 in science.
For more information, please see his CV, dblp, and Google Scholar.
Check out some of his lectures on contextual optimization and fair pricing.
For media coverage, see articles in Politico, 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)
Abdellah Aznag (co-advised with Rachel Cummings)
Haofeng Zhang (Ph.D., co-advised with Henry Lam), 2024, Machine Learning Researcher at Morgan Stanley
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 → AI Scientist at GE Healthcare
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 → Assistant Professor at Erasmus University, Rotterdam School of Management
Ryan McNellis (Ph.D.), 2020, Applied Scientist at Amazon
Yunjie Sun (Ph.D.), 2019, Senior Data Scientist at Tripadvisor → Optimization Expert 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
Fair Fares for Vehicle Sharing Systems, with Hyemi Kim [code]
• Finalist for Hyemi Kim, INFORMS DEI Best Student Paper Award, 2024
• Finalist for Hyemi Kim, INFORMS Transportation Science and Logistics Society Best Student Paper Award, 2024Simple Policies for Joint Pricing and Inventory Management, with Harsh Sheth and Yeqing Zhou [code]
Static Pricing Guarantees for Queueing Systems, with Jacob Bergquist [code]
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, George Nicholson Student Paper Competition, 2023The Power of Static Pricing for Reusable Resources, with Jiaqi Shi [code]
Matchmaking Strategies for Maximizing Player Engagement in Video Games, with Mingliu Chen and Xiao Lei
• Honorable Mention for Xiao Lei (part 2 of 3), 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), 2022The Value of Flexibility from Opaque Selling, with David D. Yao and Yeqing Zhou [code]
Retailing with Opaque Products, with Yehua Wei and Yeqing Zhou [code]
Publications
An Active Learning Framework for Multi-Group Mean Estimation, with Abdellah Aznag and Rachel Cummings
Neural Information Processing Systems (NeurIPS), 2023Generalization 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 (NeurIPS), 2019Market Segmentation Trees, with Ali Aouad, Kris J. Ferreira, and Ryan McNellis
Manufacturing & Service Operations Management, 2023 [code]Balanced Off-Policy Evaluation for Personalized Pricing, with Vishal Gupta and Yunfan Zhao
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023 [code]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), 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 ReviewRevenue 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]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), 2021Static 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, 2019Smart "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
• Appeared in INFORMS Analytics Collections Vol. 16: Advances in Integrating AI & O.R.
• Featured article, see discussion in Management Science ReviewThe 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), 2019Loot 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), 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, 2019The 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 ReviewDecision 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]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 the Practice of Advanced Analytics and Operations Research, 2019
• Finalist (part 2 of 2), INFORMS Revenue Management and Pricing (RMP) Practice Award, 2019A 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)Supply Chain Management with Online Customer Selection, with Retsef Levi
Operations Research, 2016 [code]The Submodular Joint Replenishment Problem, with Maurice Cheung, Retsef Levi, and David B. Shmoys
Mathematical Programming, 2016From Cost Sharing Mechanisms to Online Selection Problems, with Retsef Levi
Mathematics of Operations Research, 2015
• INFORMS President's Pick for October 2015New Approached for Integrating Revenue and Supply Chain Management
Massachusetts Institute of Technology Ph.D. Thesis, 2014Maximizing 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)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, 2025
IEOR 4650, Business Analytics (B.S.), Spring 2016-2018, 2021, 2023
IEOR 4650, Business Analytics (M.S.), Spring 2016-2018, 2019 (x2), 2022-23, 2025, Fall 2020
IEOR 8100, Supply Chain Management (Ph.D.), Spring 2016
IEOR 8100, Contextual Optimization (Ph.D.), Fall 2019, Fall 2024