Christopher Kennedy joins us as a technical advisor. His academic background focused on mathematics, with additional experience in various subfields of theoretical computer science. He recently graduated from the University of Texas at Austin with a PhD in Mathematics, where his dissertation research focused on hashing algorithms, regression analysis and convex optimization. He is excited to bring a working knowledge of research level machine learning and computer science to the field of patent law.