The goal of the project High-Dimensional Data Processing Using Sample Compression and Dimensionality Reduction is to design efficient techniques for dimensionality reduction and data compression of high-volume and high-dimensional datasets, with mathematically proven guarantees. This includes investigating advanced aspects of random projections, reducing dimensionality for generalized distances, and sample compression for various distance-like functions.