Active learning differs from passive "learning from examples" in that the learning algorithm itself attempts to select the most informative data for training. Since supervised labeling of data is expensive, active learning attempts to reduce the human effort needed to learn an accurate result by selecting only the most informative examples for labeling. Our work has focused on diverse ensembles for active learning and applications of active learning to problems in natural-language processing and semi-supervised learning. We have also addressed the problem of actively acquiring the most useful features values of examples as well as supervised class labels.