FRE Special Seminar: Amine Bennouna

Amine Bennouna

Assistant Professor of Operations at the Kellogg School of Management, Northwestern University

Title

What Data Enables Optimal Decisions? A Study of Data Informativeness in Optimization Under Uncertainty

Abstract

We study the fundamental question of how informative a dataset is for solving a given decision-making task. In our setting, a dataset provides partial information about unknown parameters of the decision-making problem. Focusing on linear programs with uncertainty in the cost vector, we characterize when a dataset is sufficient to recover an optimal decision. Our main contribution is a sharp geometric characterization that reveals how the task structure and the uncertainty jointly determine data requirements. Building on this characterization, we develop a practical algorithm that identifies, for a given task, a minimal sufficient dataset whose acquisition guarantees finding the optimal decision. Our results demonstrate that small, carefully selected data can often fully determine optimal decisions, providing a principled foundation for task-aware data selection. We apply our approach to the problem of selecting where to conduct field experiments to inform infrastructure design.

Bio

Amine Bennouna is an Assistant Professor of Operations at the Kellogg School of Management, Northwestern University. He earned his PhD in Operations Research from MIT and his BSc and MSc in Applied Mathematics from École Polytechnique. His research studies how data informs decisions in operations problems, with the goal of developing decision-focused experimental design methods and reliable algorithms for data-driven decision-making.