Abstract
Robust statistics aims to provide methods for reliable estimation and inference when data are generated from a distribution with some form of contamination. In this talk, we will provide a self-contained overview of some key concepts in classical robust statistics. We will then discuss a few recent results where classical concepts have proven useful in more modern settings, including heterogenous distributions, new forms of contamination, and private hypothesis testing.