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.
University of Texas at Austin, Ph.D., Mathematics, 2018
Princeton University, B.S., Mathematics, Certificate in Applications of Computing, 2013
Approximating the little Grothendieck problem over the orthogonal and unitary groups. (A.S. Bandeira, C. Kennedy, and A. Singer), Mathematical Programming, 2016
Fast cross-polytope locality-sensitive hashing. (C. Kennedy and R. Ward), Innovations in Theoretical Computer Science, 2017