I study the geometry of robot motion for manipulation, especially the Riemannian geometry of the motion optimization problem, how it interacts with workspace geometry in the presence of obstacles, and how it relates to second-order methods for nonlinear constrained optimization. Earlier work in these areas include algorithms for motion planing using fast trajectory optimizers (CHOMP) and methods for learning local mappings to latent Euclidean spaces for control. I'm currently working to generalize these ideas into a unified optimization framework called Riemannian Motion Optimization (RieMO) with specific applications to the dual arm manipulation platform Apollo at the Max Planck Institute for Intelligent Systems Autonomous Motion Department. I completed my Ph.D. work at Carnegie Mellon University’s Robotics Institute under Professor J. Andrew Bagnell in 2009. Since then I've been at TTI-C on the University of Chicago Campus building robots, Intel Labs in both Seattle and Pittsburgh studying trajectory optimization, and Google developing large scale learning systems to assess the quality of Ad Landing Pages. I'm currently part of Stefan Schaal's the Autonomous Motion Department (AMD) at the Max Planck Institute of Intelligent Systems in Tübingen, and I collaborate closely with Marc Toussaint's Machine Learning and Robotics lab at the University of Stuttgart where I have taught courses on Advanced Robotics and Mathematics for Intelligent Systems.