Ali Aouad

My research interests are in the interface of operations management, computer science, and economics. My work focuses on the design and evaluation of algorithms and digitization processes to match (or align) demand and supply taking into account (1) individual preferences and choice behaviors, and (2) dynamic operational or market environments. More specifically I have worked on issues with applications covering supply chains, digital platforms in the gig economy, and public sector areas that include food security management and cultural institutions. 

I am an associate editor in Operations Research and Management Science. In 2018-2019, 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)