Research I am advised by the following unordered pair: {Geoff Gordon, André Platzer}. Currents interests are: complementarity problems convex optimization and analysis planning and MDPs learning theory and machine learning probabilistic inference I am currently working on methods for incorporating function approximation into solution methods for a broad class of linear complementarity problems. These complementarity problems are strongly connected to the Karush-Kuhn-Tucker conditions for linear and quadratic programs, but are more general. Applications include approximating Markov decision processes and approximating support vector machines. Approximate solution methods include projected-gradient descent, proximal methods, and interior point methods. I am a member of the Logical Systems Lab and the SELECT Lab. I have also been helping a CMU/JHU APL team out with verifying the FAA's new Airborne Collision Avoidance System (ACAS X), which is based on an MDP.