Ali Aouad

Brief research statement

My research focuses on three areas: 

Assortment optimization and choice modeling: Many operating systems aim to align supply decisions with population preferences. This requires modeling and estimating stochastic choice preferences, then designing an assortment (often a small menu of options) that optimizes the planner’s objective or welfare. This question arises in various settings: retail, revenue management, marketplaces, digital platforms, food systems, recommendation engines, and modern AI systems. It raises fundamental questions in stochastic modeling and optimization, which I have explored in my research, alongside industry applications.

Matching-based market design: Matching platforms (such as labour marketplaces) often operate in dynamic and uncertain market environments, which complicates the matching of demand and supply and the management of market thickness. I have studied new modeling approaches for dynamic matching systems and obtained insights into the design of matching policies and algorithms. I have also explored market design issues of interest to both operators and regulators---who control prices, unintended effects of competition on operations, and risks of algorithm collusion. This work is motivated by industry engagement with ride-hailing platforms like Uber or Bolt.

Public and societal operations: Digitization can also enable public sector organizations to better evaluate their programs, target beneficiaries, or allocate resources. Yet the diffusion of data-driven operations in many public sector areas is lacking. I have been fortunate to explore issues related to the management of food security, cultural institutions, and water resources within the global context. For example, a close collaboration with the Van Gogh Museum enabled the use of data-driven operations and digital systems to enhance curation and visitor experience management. I have explored issues related to the design and evaluation of food subsidy mechanisms in India for low-income communities. My work in this area aims to combine technology-driven interventions with field-based evidence.

Publications and pre-prints

• Adaptive Approximation Schemes for Matching Queues, AmaniHamedani, A., and Saberi, Accepted in STOC '25

• Improved Approximations for Stationary Matching: Beyond Probabilistic Independence, AmaniHamedani, A., Pollner, and Saberi, Working paper

• Digital Recommendations Reduce Museum Fatigue: Experimental Evidence from the Van Gogh Museum, A., Deshmane, Martinez-de-Albéniz, and van Dam, Working paper 

Runner-up in the 2025 Revenue Management & Pricing Impact Prize

• Food Subsidies and Substitution: A Field Experiment Using Digitized Micro-Grocery Transactions in Underserved Communities, A., Ramdas, and Sungu, Working paper
  Entrant: Alp Sungu: Finalist in the 2024 POMS Applied Research Challenge, Runner-up in 2023 INFORMS TIMES Best Working Paper Award, First Prize in 2023 INFORMS Revenue Management & Pricing and 2023 MSOM Student Paper Competitions, Finalist in 2023 INFORMS Public Sector OR Best Paper Award

• Designing Layouts for Sequential Experiences: Application to Cultural Institutions, A., Deshmane, and Martínez-de-Albéniz, Accepted for Publication in Management Science (2025)
  Entrant: Abhishek Deshmane: Finalist in the 2024 POMS Applied Research Challenge, Second Prize in 2022 Revenue Management & Pricing Student Paper Competition, First Prize in IBM Service Science Student Paper Competition 

• Spatial Matching under Multihoming, AmaniHamedani, A., and Freund, Working paper (2023)

• Centralized versus Decentralized Pricing Controls for Dynamic Matching Platforms, A., Saritac, and Yan, Working paper (2023) [code]
  Appeared in the 24th ACM Conference on Economics and Computation (EC), 2023

• A Nonparametric Framework for Online Stochastic Matching with Correlated Arrivals, A. and Ma, Working paper (2022)

 Appeared in the 24th ACM Conference on Economics and Computation (EC), 2023

• Representing Random Utility Choice Models with Neural Networks, A. and Desir, Accepted for publication in Management Science (2022) [code]
  Second Prize in Junior Faculty Interest Group (JFIG) Paper Competition, 2022

• Algorithmic Collusion in Assortment Games, A. and den Boer, Working paper (2021)
  EC 2021 Workshop on the Design of Online Platforms: Frontiers and Challenges

• The Click-Based MNL Model: A Framework for Modeling Click Data in Assortment Optimization, A., Feldman and Segev, Accepted for publication in Management Science  (2022)
  Spotlight  track, Revenue Management and Pricing Conference, 2019 

• Market Segmentation Trees, A., Ferreira, Elmachtoub and McNeillis, Forthcoming in M&SOM (2023) [code]

• The Exponomial Choice Model for Assortment Optimization: An Alternative to the MNL Model?, A., Feldman and Segev, Forthcoming in Management Science (2023)

• Dynamic Stochastic Matching Under Limited Time, A. and Saritac, Operations Research (2022) [code]
  Appeared in The 21st ACM Conference on Economics and Computation (EC), 2020 

• Online Assortment Optimization for Two-sided Matching Platforms, A. and Saban, Management Science (2022)  [code]
  Appeared in The 22nd ACM Conference on Economics and Computation (EC), 2021
  Spotlight  track, Revenue Management and Pricing Conference, 2021 

• Technical Note -- An Approximate Dynamic Programming Approach for the Incremental Knapsack Problem, A. and Segev, Forthcoming in Operations Research (2022)

• The Stability of MNL-Based Demand under Dynamic Customer Substitution and its Algorithmic Implications, A. and Segev, Accepted in Operations Research (2022) [code]

• Display Optimization for Vertically Differentiated Locations Under Multinomial Logit Preferences,  A. and Segev, Management Science (2020)

• Assortment Optimization Under Consider-then-Choose Choice Models, A., Farias and Levi,  Management Science  (2020)

• Greedy-Like Algorithms for Dynamic Assortment Planning Under Multinomial Logit Preferences, A., Levi and Segev, Operations Research (2018)
  Finalist in the 2021 M&SOM Best OM Paper Published in Operations Research, 2016 INFORMS Student Paper Nicholson Prize 

• The Ordered k-Median Problem: Surrogate Models and Approximation Algorithms, A. and Segev, Mathematical Programming (2019)

• Approximation Algorithms for Dynamic Assortment Optimization Models, A., Levi and Segev, Mathematics of Operations Research (2018) 

• Technical Note -- The Approximability of Assortment Optimization Under Ranking Preferences, A., Farias, Levi and Segev, Operations Research (2018)