Garrett Ethan Katz
Assistant Professor
Electrical Engineering & Computer Science
- 4-189 CST
- (315) 443-3565
- gkatz01@syr.edu
Degrees:
- B.A. Philosophy, Cornell University, 2007
- M.A. Mathematics, City College of New York, 2011
- Ph.D. Computer Science, University of Maryland, College Park, 2017
Research interests:
- Neural Computation
- Cognitive Robotics
- Dynamical Systems
Current Research:
My current research focuses on programmable neural networks: neural networks that can be “programmed” like a conventional computer to execute symbolic, cognitive-level tasks, but can then refine that procedural knowledge by learning from examples and experience. One application of this work is in robotic imitation learning: “programming” robots from a single human demonstration of a task that requires high-level planning and reasoning. A second application of this work is modeling the neural basis of cognition, and cognitive disorders, in humans.
In other research I am developing new solution methods for fixed point location in recurrent neural networks and other dynamical systems, including gradient flows of optimization objective functions. This work applies broadly to solving non-linear systems of equations and non-convex optimization. I have also worked on methods for computational tomography of biological virus particles.
Teaching Interests:
My teaching interests include machine learning and artificial intelligence, especially neural computation and automated planning, as well as dynamical systems, robotics, and human-robot interaction.
Honors:
- Best student paper award at the 9thInternational Conference on Artifical General Intelligence, 2016
- Distinguished Graduate Student Teacher, University of Maryland, 2014
Recent Publications:
- Katz GE, Reggia JA (2017). Using Directional Fibers to Locate Fixed Points of Recurrent Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. DOI 10.1109/TNNLS.2017.2733544
- Katz GE, Huang DW, Hauge T, Gentili RJ, Reggia JA (2017). A Novel Parsimonious Cause-Effect Reasoning Algorithm for Robot Imitation and Plan Recognition. IEEE Transactions on Cognitive and Developmental Systems. IEEE. DOI 10.1109/TCDS.2017.2651643