My research lies in the fields of optimization and stochastic modeling, and focuses on the design and performance analysis of algorithms with applications to operations management. I am broadly interested in resource allocation problems in connection with choice modeling, revenue management, and matching markets.
I received a PhD in Operations Research from Massachusetts Institute of Technology (MIT), where my advisors were Profs. Vivek Farias and Retsef Levi. Before MIT, I earned an MS in Applied Mathematics from the Ecole Polytechnique, in Paris. I grew up in Meknes, Morocco.
Alongside academia, I have worked and consulted for technology, quantitative finance and management consulting firms. Before joining LBS, I spent a year at the Marketplace Optimization and Data Science Group at Uber Technologies (San Francisco), where I contributed to the design and implementation of new algorithms that improve the cost efficiency of the matching platform.
Keywords: Assortment and Inventory Optimization, Choice Modeling, Facility Location, Dynamic Matching, Approximation Algorithms, Stochastic Modeling
- The Ordered k-Median Problem: Surrogate Models and Approximation Algorithms, A. and Segev, Mathematical Programming (2018)
- 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)
- Assortment Optimization Under Consider-then-Choose Choice Models, A., Farias and Levi, Under Review (2020, Minor revision in Management Science) - Co-winner of the MIT-ORC Best Student Paper Competition
- Display Optimization for Vertically Differentiated Locations Under Multinomial Logit Preferences, A. and Segev, Under Review (2020, Minor revision in Management Science)
- The Exponomial Choice Model: Algorithmic Frameworks for Assortment Optimization and Data-Driven Estimation Case Studies, A., Feldman and Segev, Major Revision in Management Science (2019)
- Click-Based MNL: Algorithmic Frameworks for Modeling Click Data in Assortment Optimization, A., Feldman and Segev, Under review (2019) - Spotlight track, Revenue Management and Pricing Conference (2019)
- Market Segmentation Trees, A., Ferreira, Elmachtoub and McNeillis, Under review (2020)
- Dynamic Stochastic Matching Under Limited Time, A. and Saritac, Working paper (2019)
- The Stability of MNL-Based Demand under Dynamic Customer Substitution and its Algorithmic Implications, A. and Segev, Under review (2019)