My research focuses on the design of demand-driven operational systems and matching platforms, using tools from algorithm design, optimization, stochastic modeling, and machine learning. I am interested in two main issues: (1) Developing new methods and operational applications of discrete choice modeling and assortment optimization, and (2) Designing and analysing control algorithms for matching platforms in dynamic markets.
I received a PhD in Operations Research from Massachusetts Institute of Technology (MIT) in 2017, 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 (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 with the Matching Science team to design and experiment novel algorithms and product features, improving the efficiency of the matching process.
Here is a link to my Google Scholar profile.
Keywords: Assortment and Inventory Optimization, Choice Modeling, Facility Location, Dynamic Matching, Algorithmic Collusion, Approximation Algorithms
Online Assortment Optimization for Two-sided Matching Platforms, A. and Saban, Major Revision in Management Science (2021)
- Extended abstract accepted in The 22nd ACM Conference on Economics and Computation (EC), 2021
- Spotlight track, Revenue Management and Pricing Conference, 2021
An Approximate Dynamic Programming Approach for the Incremental Knapsack Problem, A. and Segev, Minor Revision in Operations Research (2020)
- Extended abstract accepted in The 21st ACM Conference on Economics and Computation (EC), 2020
The Stability of MNL-Based Demand under Dynamic Customer Substitution and its Algorithmic Implications, A. and Segev, Major Revision in Operations Research (2020)
The Click-Based MNL Model: A Novel Framework for Modeling Click Data in Assortment Optimization, A., Feldman and Segev, Under review, R&R in Management Science (2020)
- 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, Minor Revision in Management Science (2021)
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)
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)