Ali Aouad

Ali Aouad's research interests are at the interface of operations management, computer science and economics. His work broadly studies data-driven decision processes with applications covering supply chains, market design, and public sector issues. His first line of research aims to advance the theory of algorithm design under uncertainty in digital markets, with a focus on matching and assortment decisions. His second line of research studies the interplay of digitization, individual preferences and behaviours, and data-driven policies in the specific contexts of food security management and cultural institutions, and includes primary data collection and field experiments.

He is an associate editor for Operations Research and Management Science and has served on ACM program committees. Before MIT, Ali was an Associate Professor at the London Business School. Ali holds a PhD in Operations Research from MIT and an MS and BS in Applied Mathematics from Ecole Polytechnique (Paris).

Alongside academia, Ali has worked and consulted for technology firms. In 2017-2018, he was an applied scientist at Uber Technology’s Marketplace (San Francisco), where he developed product innovations for matching and pricing technology. Prior work experiences include public sector strategic consulting at the Boston Consulting Group (Paris and Casablanca).

Selected research awards:

2024 Winner of 2024 POMS Applied Research Challenge for “Designing Layouts for Sequential Experiences: Application to Cultural Institutions”

2024 Finalist in 2024 POMS Applied Research Challenge for “Food Subsidies and Substitution: Experimental Evidence from Indian Micro Retail Panel Data”

2023 “Food Subsidies and Substitution: Experimental Evidence from Indian Micro Retail Panel Data”: Runner-up in 2023 INFORMS TIMES Best Working Paper Award; Finalist in 2023 INFORMS Public Sector OR Best Paper Award

2023 Selected for funding by the 2022 ERC Starting Grant panel for “Pathway Operations: Theory and Application to Museums” (£1.1M)

2022 2nd prize of INFORMS Junior Faculty Interest Group for: “Representing Random Utility Choice Models using Neural Networks"

2021 Finalist of INFORMS Best OM paper in Operations Research for: “Greedy-like Algorithms for Dynamic Assortment Planning under Multinomial Logit Preferences”