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

aaouad AT london.edu

+44(0)20 7000 8858

My research spans two main themes: (1) Developing new data-driven methods for assortment optimization and discrete choice modeling, and (2) Designing approximation algorithms for large-scale resource allocation problems in dynamic, data-driven environments such as matching platforms. Recently, I explore new applications of these methods to cultural institutions.

I received a PhD in Operations Research from Massachusetts Institute of Technology (MIT) in 2017, where my supervisors were Profs. Vivek Farias and Retsef Levi. I was also very fortunate to collaborate with and be advised by Prof. Danny Segev. Before MIT, I earned an MS in Applied Mathematics from the Ecole Polytechnique (Paris) in 2013. I grew up in Meknes, Morocco.

Before joining LBS, I was a data scientist in the Marketplace Optimization group at Uber Technologies in San Francisco (2017-2018). I worked and consulted for the Matching Science team to design and experiment novel algorithms and product features, improving the efficiency of the matching process.

Research Papers

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

Preprints

Algorithmic Collusion in Assortment Games, A. and den Boer, Under review

- Accepted in the EC 2021 Workshop on the Design of Online Platforms: Frontiers and Challenges

Market Segmentation Trees, A., Ferreira, Elmachtoub and McNeillis, Under revision (2020) [code]

The Stability of MNL-Based Demand under Dynamic Customer Substitution and its Algorithmic Implications, A. and Segev, Under review (2020)

The Click-Based MNL Model: A Novel Framework for Modeling Click Data in Assortment Optimization, A., Feldman and Segev, Under revision (2021)

- 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 (2021)

Journal Papers

Online Assortment Optimization for Two-sided Matching Platforms, A. and Saban, Forthcoming in Management Science (2022)

- Extended abstract accepted in The 22nd ACM Conference on Economics and Computation (EC), 2021

- Spotlight track, Revenue Management and Pricing Conference, 2021

Dynamic Stochastic Matching Under Limited Time, A. and Saritac, Forthcoming in Operations Research (2022) [code]

- Extended abstract accepted in The 21st ACM Conference on Economics and Computation (EC), 2020

An Approximate Dynamic Programming Approach for the Incremental Knapsack Problem, A. and Segev, Forthcoming in Operations Research (2021, Technical Note)

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)

- 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, Technical Note)

Greedy-Like Algorithms for Dynamic Assortment Planning Under Multinomial Logit Preferences, A., Levi and Segev, Operations Research (2018)[code]

- Finalist in the 2021 MS&OM Best OM Paper Published in Operations Research

- Finalist in the 2016 INFORMS Student Paper Nicholson Prize

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

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


Other Interests

Painting, Literature, Boxing