This talk focuses on active ML that close the loop on machine learning, sensing and data collection, and human labeling. Standard (passive) machine learning involves designing a classification rule based on a randomly selected training dataset. Active machine learning algorithms automatically and adaptively select the most informative data for labeling so that human time is not wasted labeling irrelevant or trivial examples. The aim is to make ML as efficient and robust as possible, with a minimal amount of human supervision and assistance. This talk describes ongoing theoretical and experimental work in several areas of active learning