Ali Aouad

Assistant Professor at the London Business School (2018-Present)

Management Science and Operations

Regent's Park, London, United Kingdom (NW14SA)

aaouad AT london.edu

+44(0)20 7000 8858

My primary research interests are in the areas of algorithm design and dynamic optimization with applications to retail analytics and matching platforms. My research spans two major themes: (1) customer choice modeling in operational contexts, and (2) dynamic resource allocation problems related to online platforms and networks.

I received a PhD in Operations Research from Massachusetts Institute of Technology (MIT), where my advisors were Profs. Vivek Farias and Retsef Levi. I was also very fortunate to collaborate with Prof. Danny Segev. Before MIT, I earned an MS in Applied Mathematics from the Ecole Polytechnique, in Paris. I grew up in Meknes, Morocco.

Before joining LBS, I spent a year in the Marketplace Data Science group at Uber Technologies in San Francisco. I worked and consulted with the Matching Science team to design and implement innovative algorithms and product features, improving the efficiency of the platform.

Here is a link to my Google Scholar profile.

Research Papers

Keywords: Assortment and Inventory Optimization, Choice Modeling, Facility Location, Dynamic Matching, Approximation Algorithms

Preprints

Online Assortment Optimization for Two-sided Matching Platforms, A. and Saban, Working paper (2020)

An Approximate Dynamic Programming Approach for the Incremental Knapsack Problem, A. and Segev, Under Review (2020)

Dynamic Stochastic Matching Under Limited Time, A. and Saritac, Minor Revision in Operations Research (2020) - Extended abstract accepted in The 21st ACM Conference on Economics and Computation (EC), 2020

Market Segmentation Trees, A., Ferreira, Elmachtoub and McNeillis, Major Revision in MS&OM (2020)

The Stability of MNL-Based Demand under Dynamic Customer Substitution and its Algorithmic Implications, A. and Segev, Major Revision in Operations Research (2020)

Click-Based MNL: Algorithmic Frameworks for Modeling Click Data in Assortment Optimization, A., Feldman and Segev, R&R in Management Science (2019) - Accepted in the Spotlight track, Revenue Management and Pricing Conference, 2019

The Exponomial Choice Model: Algorithmic Frameworks for Assortment Optimization and Data-Driven Estimation Case Studies, A., Feldman and Segev, Under Review (2020, Major Revision in Management Science)

Journal Papers

Display Optimization for Vertically Differentiated Locations Under Multinomial Logit Preferences, A. and Segev, Accepted for Publication in Management Science

Assortment Optimization Under Consider-then-Choose Choice Models, A., Farias and Levi, Accepted for Publication in Management Science - Co-winner of the MIT-ORC Best Student Paper Competition

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

Greedy-Like Algorithms for Dynamic Assortment Planning Under Multinomial Logit Preferences, A., Levi and Segev, Operations Research (2018) - Finalist in the 2016 INFORMS Nicholson Prize

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

The Ordered k-Median Problem: Surrogate Models and Approximation Algorithms, A. and Segev, Mathematical Programming (2018)