Whether in robotics, protein design, or physical sciences, one often faces decisions regarding which data to collect or which experiments to perform. There is thus a pressing need for algorithms and sampling strategies that make intelligent decisions about data collection processes that allow for data-efficient learning. Experimental design and active learning have been major research focuses within machine learning and statistics, aiming to answer both theoretical and algorithmic aspects of efficient data collection schemes. The goal of this workshop is to identify missing links that hinder the direct application of these principled research ideas into practically relevant solutions.