Ali Aouad

My research focuses the design of algorithms and decision processes in stochastic environments, with particular attention to interactions with human behavior and economic preferences. I study applications to market design, supply chains, and public-sector operations, with intended contributions ranging from theoretical foundations to collaborations with private- and public-sector organizations for field deployment and evidence-based evaluation. 

I serve as an associate editor in Operations Research and Management Science.  I am a principal investigator in the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) and MIT Sloan's Food Supply Chain Analytics and Sensing Initiative (FSAS), and an invited researcher at the Abdul Latif Jameel Poverty Action Lab (J-PAL).

Before MIT, I was an Associate Professor at London Business School (2018-2024) and an applied scientist and external consultant in the Marketplace Optimization group at Uber Technologies (2018-2020), with prior experience in the technology sector and public-sector consulting. I received a PhD in Operations Research from the Massachusetts Institute of Technology (MIT) in 2017. Before MIT, I earned an MS in Applied Mathematics from the École Polytechnique (Paris) in 2013.  I was born in Meknes, Morocco.

Publications and pre-prints

• The Sign Estimator: LLM Alignment in the Face of Choice Heterogeneity, A., El Gadarri, and Farias, Working paper

Accepted in ICML 2026

• Human-AI Productivity Paradoxes: Modeling Skill, Effort, and AI Assistance, A., Lykouris, Zhong, Working paper Accepted in the 27th ACM Conference on Economics and Computation (EC), 2026

• Improved Approximations for Stationary Matching: Beyond Probabilistic Independence, AmaniHamedani, A., Pollner, and Saberi, Working paper   Accepted in the 27th ACM Conference on Economics and Computation (EC), 2026

• The Popeye Effect: Social Media Influence and Healthy Food Shopping in Informal Micro-Retail Markets, A., Ramdas, Sungu, Gupta, Kesebir, Swaminathan, Working paper

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

• 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)