The focus of our research is intelligent control in biology and engineering. We believe that the key to achieving dynamic intelligence is optimization. In biology, motor behavior is shaped by processes such as evolution, learning and adaptation that resemble iterative optimization. In engineering, perhaps the best way to build a truly complex controller that actually works is to specify a high-level performance criterion, and leave the details of the design process to numerical optimization. We are pursuing multiple lines of research spanning many traditional disciplines: control engineering, computer science, robotics, neuroscience, psychology, (bio) mechanics, applied mathematics. Despite their interdisciplinary nature, all these efforts are aimed at a common goal: understanding and synthesizing dynamic intelligence through learning and optimization.