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