- 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
Areas of Expertise:
- Automated Planning
- Automated Program Induction and Synthesis
- Robotic Manipulation
- Neural Computation
My research focuses on “vertically integrated” artificial intelligence, ranging from low-level robotic motor control and synaptic learning rules to high-level planning and abstract reasoning. My recent work has focused on neurocomputational systems for cognitive-level robotic imitation learning.
Honors and Awards:
- Best Paper Award at the SAI Computing Conference, 2020
- Larry S. Davis Doctoral Dissertation Award, UMD, 2018
- Best Student Paper Award at the 9th International Conference on Artificial General Intelligence 2016
- Katz GE, Tahir N. Towards Automated Discovery of God-Like Folk Algorithms for Rubik’s Cube. In 2022 AAAI Conference on Artificial Intelligence. AAAI.
- Katz GE, Akshay, Davis GP, Gentili RJ, Reggia JA. Tunable Neural Encoding of a Symbolic Robotic Manipulation Algorithm. Frontiers in Neurorobotics. 2021:167.
- Tahir N, Katz GE. Numerical Exploration of Training Loss Level-Sets in Deep Neural Networks. In 2021 International Joint Conference on Neural Networks (IJCNN) 2021 (pp. 1-8). IEEE.
- Katz GE, Gupta K, Reggia JA. Reinforcement-based Program Induction in a Neural Virtual Machine. In 2020 International Joint Conference on Neural Networks (IJCNN) 2020 (pp. 1-8). IEEE.