Over the past few years, there has been exciting progress on algorithmic robust statistics in unsupervised, supervised and online learning. Much of this progress has been fueled by new algorithmic tools for detecting portions of the samples that have different distributional profiles. We will survey some of these tools as well as discuss prospects for building theories of coping with distribution shift from them.