Robust optimization & applied probability

Robust optimization has emerged as a tractable paradigm for analyzing and optimizing models in which certain system parameters are unknown. Alternatively, applied probability has traditionally been a field yielding insights into how systems behave under uncertainty, when the uncertainty is characterized by known random variables. New modeling and optimization challenges, e.g. those coming from data-rich environments and arising in the domain of machine learning, give rise to questions best addressed by combining insights from both domains of robust optimization and applied probability. The aim of this workshop is to bring together leaders whose expertise spans these areas of research, facilitating the transference of ideas, insights, and methodologies to tackle new and exciting problems at the interface of robust optimization and applied probability.

 

Confirmed speakers:

 

Aharon Ben-Tal (Technion)

Dick den Hertog (Tilburg University)

Ger Koole (VU Amsterdam)

Ton de Kok (TU Eindhoven)

Daniel Kuhn (EPFL)

Shie Mannor (Technion)

Melvyn Sim (National University of Singapore)

For more information and registration visit the Eurandom website