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. 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 postdoc at the IBM T.J. Watson Research Center working in the area of Smarter Commerce. 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 U.S News, NewsWise, ConsumerAffairs, GamesIndustry, PC Gamer, Wired, and Columbia Spectator.

I co-founded and co-organized NYC Operations Day, see event pages for 2018, 2019, and 2020.

Here is a recent one hour invited talk at CPAIOR 2021 called Contextual Optimization: Bridging Machine Learning and Operations Research. I am co-organizing the Master Class at CPAIOR 2022 on related topics, schedule can be found here.

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.

Yunfan Zhao

Mingliu Chen (Postdoc)

Harsh Sheth (co-advised with Vineet Goyal)

Xiao Lei

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

Ryan McNellis (Ph.D.), 2019, 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], Dassault Falcon Jet, IBM, and Columbia University.

Under Review

  1. Matchmaking Strategies for Maximizing Player Engagement in Video Games, with Mingliu Chen and Xiao Lei

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

  3. The Value of Flexibility from Opaque Selling, with David D. Yao and Yeqing Zhou
    Major revision in Management Science

  4. Market Segmentation Trees, with Ali Aouad, Kris J. Ferreira, and Ryan McNellis
    Major revision in Manufacturing & Service Operations Management [code]

  5. Generalization Bounds in the Predict-then-Optimize Framework, with Othman El Balghiti, Paul Grigas, and Ambuj Tewari
    Accepted to Neural Information Processing Systems 32 (NeurIPS), 2019

  6. Retailing with Opaque Products, with Yehua Wei and Yeqing Zhou
    Major revision in Manufacturing & Service Operations Management

Publications

  1. Price Discrimination with Fairness Constraints, with Maxime C. Cohen and Xiao Lei
    Management Science, forthcoming [code]
    Accepted to The 4th ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2021
    Oral presentation at The 4th Workshop on Mechanism Design for Social Good (MD4SG), 2020

  2. Smart "Predict, then Optimize", with Paul Grigas
    Management Science, forthcoming [talk with Paul] [code]
    1st place, INFORMS Junior Faculty Interest Group (JFIG) Paper Competition, 2020

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

  4. 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 in The 15th Conference on Web and Internet Economics (WINE), 2019

  5. Loot Box Pricing and Design, with Ningyuan Chen, Michael L. Hamilton, and Xiao Lei
    Management Science, 2021 [talk by Xiao]
    Third Prize, CSAMSE Annual Conference Best Paper Award Competition, 2021
    Accepted in The 21st ACM Conference on Economics and Computation (EC), 2020
    Invited to present at the Federal Trade Commission (FTC), 2019
    1st place for Xiao Lei, INFORMS IBM Best Student Paper Award in Service Science, 2019

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

  7. 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]

  8. 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

  9. 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)

  10. Supply Chain Management with Online Customer Selection, with Retsef Levi
    Operations Research, 2016

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

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

  13. 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)

  14. 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

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).

Segmentation based estimation method for demand models under censored data, with Markus R. Ettl, Sechan Oh, Marek Petrik, and Rajesh K. Ravi. US Patent 2018/0060885 (published).

Revenue management using dynamic customer selection, with Roger Lederman. US Patent 2017/0358001 (published).

Training a machine to dynamically determine and communicate customized, product-dependent promotions with no or limited historical data over a network, with Markus R. Ettl, Sechan Oh, Marek Petrik, and Rajesh K. Ravi. US Patent 2017/0046732 (published).

Teaching

IEOR 4418, Transportation Analytics and Logistics (B.S./M.S.), Fall 2016, Spring 2018-2022

IEOR 4650, Business Analytics (B.S.), Spring 2016-2018, 2021-2022

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