Conference/Workshop of interest for AIROYoungers

From: 04 Apr 2022
To: 08 Apr 2022
International PhD School,
University of Copenhagen
4-8 April 2022
The Copenhagen School of Stochastic Programming is a PhD course that provides a rigorous and research-oriented introduction to stochastic programming, a mathematical framework for decision-making in the presence of uncertainty. In many real-life problems, important information is unknown to the decision-maker and only distributional information is available. Examples include the scheduling of power generation while demand and renewable production is uncertain, investments in assets with uncertain future returns or production of goods for which demand is stochastic.

The course is taught by world-renowned experts on the subject (see below). It will start by formalizing decision problems under uncertainty as mathematical optimization problems and analyzing their fundamental mathematical properties. From a computational perspective, these problems may be extremely challenging. Thus, a major part of the course will discuss approximations, mainly of the underlying probability distributions (so-called scenario generation), and stability of the optimization problems. The course will then proceed with a number of applications in the energy sector, an area for which stochastic programming has become increasingly important with the adoption of intermittent renewable energy sources. Finally, a selection of solution methods will be addressed, including exact decomposition procedures and approximate methods with strong connections to emerging approaches in machine learning.

The course is primarily aimed at PhD students who require a solid introduction into decision making under uncertainty. It is also open to master students interested in the subject, or to post-doctoral students who need to approach the discipline. A student can earn a certificate of 3.5 ECTS upon successful completion the course. The course is open to students from all over the world and the registration is free.