Wasserstein and wavelets

The Wasserstein distance, which quantifies the minimum cost of transporting mass from one probability measure to another, plays a central role in the theory of optimal transport. More recently, it has found exciting applications in machine learning and data science. On the other hand, wavelets have important applications in signal processing, data compression and statistical estimation. In this talk, I will give a brief introduction to the Wasserstein distance, wavelets and the relationships between them. No prior knowledge of optimal transport or wavelets is assumed.