Dan's research focuses on applying knowledge about biological information processing systems to building better artificial sensorimotor systems that can adapt and learn from experience. Drawing from the ways in which biological systems compute and learn, Dan and his lab look at computational neuroscience models, theoretical foundations of machine learning algorithms, as well as constructing real-time intelligent robotic systems, with an ultimate goal of making machines that better understand what we want them to do.