Ali Aouad

My research interests lie at the intersection of algorithm design under uncertainty and operations management. My work studies data-driven decision processes for revenue management and market design within the algorithmic economy, as well as applications in the public sector, focusing on cultural institutions and food security management.

I am an associate editor in Operations Research and Management Science. Before LBS, I was an applied scientist in the Marketplace Optimization group at Uber Technologies.  I worked and consulted for the Matching Science team to design and experiment with new matching and pricing algorithms. I received a PhD in Operations Research from the Massachusetts Institute of Technology (MIT) in 2017, where I collaborated with Profs. Vivek Farias, Retsef Levi, and Danny Segev. Before MIT, I earned an MS in Applied Mathematics from the Ecole Polytechnique (Paris) in 2013.  I was born in Meknes, Morocco.

Research work

keywords: algorithm design, matching markets, choice modeling, assortment and inventory systems, food security, cultural institutions.

• Digitized Indian Micro-Grocery Transactions Reveal that Grain Subsidies Reduce Junk Food Buying by Low-Income Shoppers, 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

• 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

• Designing Layouts for Sequential Experiences: Application to Cultural Institutions, A., Deshmane, and Martínez-de-Albéniz, Under revision (2022)
  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 

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

• Algorithmic Collusion in Assortment Games, A. and den Boer, Under revision (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, Under review  (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)