Jeff McGill Student Paper Award

The INFORMS Revenue Management and Pricing (RMP) Section announces the 2023 Jeff McGill Student Paper Award. The awards are given annually for student papers judged to be the best in the field of revenue management and pricing.

The first prize is accompanied by a $500 honorarium. The second prize is accompanied by a $300 honorarium. All other finalists, if any, will be awarded $100. The number of finalists will depend on the number of entries. All prizes including finalists are accompanied by a plaque in addition to the cash award. 

Eligibility Conditions:
There are five conditions for eligibility:

  1. Entrant must have been a student on or after January 1, 2023, and the research presented in the paper must have been conducted while the entrant was a student.
  2. The submitted paper must present original research conducted primarily by the student entrant. Some assistance by other individuals (such as the student's faculty advisor) is permitted, however, the entrant's primary research advisor must certify that the student's share of contribution to the paper exceeds 50%.
  3. Entrants must be a member of the RMP Section on the date of submission.
  4. The paper must not have won a prize (1st-2nd) in a previous RMP dissertation or student paper competition.
  5. A student may submit at most one paper.

Submission Requirements:

A complete entry consists of:

  1. A Word document or a PDF stating the entrant's contact information, the contact information of the entrant's primary research advisor and all co-authors, the paper title, and appropriate keywords for the submitted paper.
  2. The paper in completely anonymous form and PDF file format, with a length of at most 32 pages including the appendix, tables, figures, and references, and strictly compliant with all submission formatting standards of the journal Operations Research or Management Science. There could be appendices beyond the 32-page limit, but the judges are not responsible for covering those materials. The file name should be the short title of the paper.
  3. An electronic PDF file of a letter signed by both a faculty advisor and the entrant attesting that the entrant and the paper satisfy the eligibility conditions.

The above three documents should be sent by the entrant as email attachments to Complete entries must be received on or before July 31st, 2023. It is the entrant's responsibility to allow for the appropriate time to enroll as a new member of the RMP section (approximately 5 business days).

2023 RMP Student Paper Award Committee

  • Ali Aouad, London Business School
  • Yonatan Gur, Stanford University
  • Krishnamurthy Iyer, University of Minnesota
  • Hongyao Ma, Columbia University
  • Vahideh Manshadi (chair), Yale University
  • Rad Niazadeh, University of Chicago
  • Marcelo Olivares, University of Chile
  • Ozge Sahin, Johns Hopkins University
  • Mika Sumida, USC

2023 Winners and Finalists
  • First Place:
    • Food Subsidies at the Base-of-the-Pyramid: Take-up, Substitution Effects and Nutrition” by Alp Sungu (London Business School)
  • Second Place:
    • Signaling Competition In Two-Sided Markets” by Yuri Fonseca (Columbia)
  • Finalist:
    • Causal Inference under Network Interference using a Mixture of Randomized Experiments” by Yiming Jiang (Georgia Tech)
    • Dynamic Resource Allocation: Algorithmic Design Principles and Spectrum of Achievable Performances” by Akshit Kumar (Columbia)

2022 Winners and Finalists
  • First Place:
    • “Markovian Interference in Experiments” by Tianyi Peng & Andrew Zheng (MIT)
  • Second Place:
    • “Designing Layouts for Sequential Experiences: Application to Cultural Institutions” by Abishek Deshmane (IESE)
  • Finalist:
    • “Multi-Item Order Fulfillment Revisited: LP Formulation and Prophet Inequality” by Ayoub Amil (Duke)
    • “Feature-based Dynamic Pricing with Online Learning and Offline Data” by Yunzong Xu & Sabrina Zhai (MIT)
    • “Model-Free Assortment Pricing with Transaction Data” by Saman Lagzi (University of Toronto)
    • “Price Discrimination with Fairness Constraints” by Xiao Lei (Columbia)
    • “Data-Driven Asset Selling” by Puping (Phil) Jiang (Washington University)
    • “Dynamic Learning in Large Matching Markets” by Anand Kalvit (Columbia)
    • “UMOTEM: Upper Bounding Method for Optimizing over Tree Ensemble Models” by Leann Thayaparan (MIT)
    • “Joint Product Design and Dynamic Assortment Optimization: Integrating Strategic and Tactical Revenue Management” by Mengxin Wang (UC Berkeley)

2021 Winners and Finalists
  • First Place:
    • “How Big Should Your Data Really Be? Data Driven Newsvendor and the Transient of Learning” by Omar Mouchtaki (Columbia)
  • Second Place:
    • “Experimental Design in Two-Sided Platforms: An Analysis of Bias” by Hannah Li (Stanford)
  • Finalist:
    • “Fair Exploration via Axiomatic Bargaining” by Jackie Baek (MIT)
    • “Tight Guarantees for Multi-unit Prophet Inequalities and Online Stochastic Knapsack” by Jiashuo Jiang (NYU)
    • “Labor Cost Free-Riding in the Gig Economy” by Zhen Lian (Cornell)
    • “Blind Dynamic Resource Allocation in Closed Networks via Mirror Backpressure” by Pengyu Qian (Columbia)

2020 Winners and Finalists
  • First Place:
    • “Design and Analysis of Switchback Experiments” by Jinglong Zhao (MIT)
  • Second Place:
    • “Engineering Social Learning: Information Design of Time-Locked Sales Campaigns for Online Platforms” by Can Küçükgül (UT Dallas)
  • Honorable Mention:
    • “Optimizing Offer Sets in Sub-Linear Time” by Deeksha Sinha (MIT)
    • “Cold Start on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments” by Zikun Ye (UIUC)
2019 Winners and Finalists
  • First Place:
    • “Dynamic Pricing of Relocating Resources in Large Networks” by Chen Chen (Duke)
  • Second Place:
    • “Decision Forest: A Nonparametric Approach to Modeling Irrational Choice” by Yi-Chun Chen (UCLA)
    • “Shapley Meets Uniform: An Axiomatic Framework for Attribution in Online Advertising” by Raghav Singal (Columbia)
  • Finalist:
    • “The Value of Price Discrimination in Large Random Networks” by Jiali Huang (Minnesota)
    • “Sequential Procurement with Contractual and Experimental Learning” by Gregory Macnamara (Stanford)
    • “Matching in Online Marketplaces when Talent is Difficult to Discern” by Jiding Zhang (Upenn)