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 9^thInternational 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