Robust optimization & Applied probability

Description

The aim of the ROAP workshop was to bring together leaders whose expertise spans two areas of

research: 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 workshop aimed at facilitating the transference of ideas, insights, and methodologies to tackle new and exciting problems at the interface of robust optimization and applied probability.

 

Format

On Sunday afternoon a meet and greet was arranged for with the international speakers. During several hours interesting future research topics were exchanged, which led to a lively discussion and an agenda for the slots during the workshop on Monday and Tuesday, that were dedicated to “discussing future research plans”.

Monday started with a tutorial lecture of Dick den Hertog on Robust optimization followed by lectures of among other Daniel Kuhn and Erick Delage. The day was closed with an open discussion session on future research directions, which led to energizing and valuable interaction. This discussion was continued during the workshop diner that evening.
On Tuesday the opening session was by Aharon Ben-Tal, the founding father of Robust optimization, followed by again a strong and lively series of lectures.