OVERVIEW AND GOAL

Many computational tasks can be naturally modeled as geometric problems in high-dimensional spaces. Data sets in areas such as optimization, computer vision, machine learning or statistics often live in spaces of dimensionality in the order of thousands or millions. However, geometric algorithms and data structures often require time or space that is exponential in the dimension.
Overcoming this "curse of dimensionality" has been a subject of extensive research over the last few decades.

The goal of the summer school is to provide an in-depth introduction to some of the key problems and techniques in high-dimensional geometric computing. The school will cover topics such as linear programming, algorithms for spaces with low intrinsic dimension, high-dimensional combinatorics and similarity search. A number of interesting open problems in the area will also be highlighted.