Nuria Gómez Vargas - I want to optimize but I don’t know what: Contextual Decision Making in Uncertain Scenarios
Abstract
Decision-making under uncertainty is a fundamental challenge in operations research, particularly in data-driven optimization. Classical optimization techniques assume a well-defined optimization problem, yet in many real-world scenarios, decision-makers face ambiguity not only in constraints and parameters but also in the very definition of what should be optimized. This talk explores the problem of contextual decision-making in uncertain environments, where decisions must adapt to hidden objectives, implicit preferences, or dynamically changing conditions that can be modeled depending on contextual information surrounding the problem. We will discuss methodologies that integrate data-driven learning with optimization, from predictive modeling to inverse optimization.
Nuria is a PhD Student at Department of Statistics and Operations Research, Facul