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

aaouad AT london.edu

+44(0)20 7000 8858

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.

Research Papers

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

  1. The Ordered k-Median Problem: Surrogate Models and Approximation Algorithms, A. and Segev, Mathematical Programming (2018)
  2. The Approximability of Assortment Optimization Under Ranking Preferences, A., Farias, Levi and Segev, Operations Research (2018)
  3. 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
  4. Approximation Algorithms for Dynamic Assortment Optimization Models, A., Levi and Segev, Mathematics of Operations Research (2018)
  5. 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
  6. Display Optimization for Vertically Differentiated Locations Under Multinomial Logit Preferences, A. and Segev, Under Review (2020, Minor revision in Management Science)
  7. 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)
  8. 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)
  9. Market Segmentation Trees, A., Ferreira, Elmachtoub and McNeillis, Under review (2020)
  10. Dynamic Stochastic Matching Under Limited Time, A. and Saritac, Working paper (2019)
  11. The Stability of MNL-Based Demand under Dynamic Customer Substitution and its Algorithmic Implications, A. and Segev, Under review (2019)