Our work is focused on machine learning: the problem of automatically building models which explain observed systems and predict their future behavior. Current research addresses adversarial learning problems, transfer learning, data science, machine learning for computer security, and pattern recognition. In adversarial learning, an adversary exercises some control over the data-generation process; this reflects many security applications of machine learning. Transfer learning algorithms learn to perform a task, but do so using training data that reflect a different task. Machine learning has many diverse applications, and we are working on some of them: security (spam, phishing, botnets), model-building in the sciences, and machine learning on embedded systems.