I have the pleasure of working with some fantastic students at Columbia. If you are interested in becoming a student, please apply here.
Harsh Sheth (co-advised with Vineet Goyal)
Ryan McNellis, 2019, Machine Learning Scientist at Amazon
Yunjie Sun, 2019, Senior Data Scientist at Tripadvisor
Michael Hamilton, 2019, Assistant Professor at University of Pittsburgh, Katz Graduate School of Business
Queuing Safely for Elevator Systems amidst a Pandemic, with S. M. Ananthanarayanan, C. C. Branas, C. Stein, and Y. Zhou
Major revision in Production and Operations Management. [animation] [code]
The Value of Flexibility from Opaque Selling, with D. D. Yao and Y. Zhou
Major revision in Management Science.
Price Discrimination with Fairness Constraints, with M. C. Cohen and X. Lei
Minor revision in Management Science. [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.
Retailing with Opaque Products, with Y. Wei and Y. Zhou
Major revision in Manufacturing & Service Operations Management.
Loot Box Pricing and Design, with N. Chen, M. L. Hamilton, and X. Lei
Management Science, forthcoming. [talk by Xiao]
• 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.
The Power of Opaque Products in Pricing, with M. L. Hamilton
Management Science, forthcoming.
• Accepted in The 13th Conference on Web and Internet Economics (WINE), 2017.
The Value of Personalized Pricing, with V. Gupta and M. L. Hamilton
Management Science, forthcoming. [code]
• Finalist, INFORMS Best Cluster Paper Award in Service Science, 2018.
• Accepted in The 15th Conference on Web and Internet Economics (WINE), 2019.
Static Pricing: Universal Guarantees for Reusable Resources, with O. Besbes and Y. 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.
Decision Trees for Decision-Making under the Predict-then-Optimize Framework, with J. C. N. Liang and R. McNellis
Proceedings of the 37th International Conference on Machine Learning (ICML), 2020. [code]
Pricing Analytics for Rotable Spare Parts, with O. Besbes and Y. Sun
INFORMS Journal on Applied Analytics, Vol. 50(5), p. 313-324, 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.
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), 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.
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 10546320, 2020.
IEOR 4418, Transportation Analytics and Logistics (B.S./M.S.), Fall 2016, Spring 2018, 2019, 2020, 2021
IEOR 4650, Business Analytics (B.S./M.S.), Spring 2016, 2017 (x2), 2018 (x2), 2019 (x2), 2021, 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