Ali Aouad

Assistant Professor Management Science and Operations

BSc MSc (Ecole Polytechnique) PhD (Massachusetts Institute of Technology)

Dr Ali Aouad's research revolves around the design and analysis of algorithms, with applications to operations management. He is broadly interested in choice modelling, revenue management and matching platforms. His work ranges from analyzing the fundamental properties of algorithms to driving their implementation in the practice.

He holds a PhD in Operations Research from Massachusetts Institute of Technology (MIT) and an MS and BS in Applied Mathematics from Ecole Polytechnique (Paris). He has research articles published in Management Science, Operations Research, Mathematics of Operations Research and Mathematical Programming. Dr Aouad was awarded the Best Student Paper Prize by the Operations Research Center at MIT in 2015, and was a finalist in the INFORMS Nicholson Prize competition in 2016.

Alongside academia, Dr Aouad has worked and consulted for various technology firms. Before joining LBS, he spent a year at Uber’s Marketplace Data Science (San Francisco), where he contributed to the design and implementation of new algorithms that improved the efficiency of the matching platform.

  • Choice Modelling
  • Inventory and Revenue Management
  • Matching Platforms
  • Approximation Algorithms
  • Stochastic Optimization


Assortment optimization under consider-then-choose choice models

Aouad A; Farias V; Levi R

Management Science 2021 Vol 67:6 p 3368-3386


Display Optimization for Vertically Differentiated Locations Under Multinomial Logit Preferences

Aouad A; Segev D

Management Science 2020 Vol 67:6 p 3519-3550 Online first

Dynamic stochastic matching under limited time

Aouad A; Saritac O

Proceedings of the 21st ACM Conference on Economics and Computation; July 2020; 789-790


Approximation algorithms for dynamic assortment optimization models

Aouad A; Levi R; Segev D

Mathematics of Operations Research 2019 Vol 44:2 p 487-511

The ordered k-median problem: surrogate models and approximation algorithms

Aouad A; Segev D

Mathematical Programming 2019 Vol 177:1-2 p 55-83


Greedy-like algorithms for dynamic assortment planning under multinomial logit preferences

Aouad A; Levi R; Segev D

Operations Research 2018 Vol 66:5 p 1321-1345

The approximability of assortment optimization under ranking preferences

Aouad A; Farias V; Levi R; Segev D

Operations Research 2018 Vol 66:6 p 1661-1669

Market Segmentation Trees

Aouad A; Elmachtoub A N; Ferreira K J; McNellis R


Online assortment optimization for two-sided matching platforms

Aouad A; Saban D

Social Sciences Research Network


Assortment optimization under consider-then-choose choice models

Aouad A; Farias V F; Levi R

Social Sciences Research Network

Click-based MNL: algorithmic frameworks for modeling click data in assortment optimization

Aouad A; Feldman J; Segev D; Zhang D

Social Sciences Research Network

Dynamic Stochastic Matching Under Limited Time

Aouad A; Saritac O

Social Sciences Research Network

Teaching portfolio

Our teaching offering is updated annually. Faculty and programme material are subject to change.