Garrett Ethan Katz, Ph.D.

Assistant Professor

Deptartment of Electrical Engineering & Computer Science

CST 4-189, Syracuse University

(315) 443-3565

gkatz01@syr.edu

Curriculum Vitae

Google Scholar

Personal site

Assistant Professor

Deptartment of Electrical Engineering & Computer Science

CST 4-189, Syracuse University

(315) 443-3565

gkatz01@syr.edu

Curriculum Vitae

Google Scholar

Personal site

I teach and research various topics in artificial intelligence, cognitive modeling, machine learning, neural computation, optimization, and robotics. My lab focuses on "vertically-integrated" AI, from low-level sensorimotor control up through high-level cognition and reasoning. Below are some representative research projects, followed by recently taught courses.

The goal of this project is to develop methods that can automatically design new algorithms, with improved optimality or complexity properties relative to known algorithms. As a starting point we have focused on state-space puzzles such as Rubik's cube, where the "algorithms" take the form of rule tables which partition the state space into subsets and specify which action sequence to perform in each subset. We formulate a multi-objective optimization problem to simultaneously minimize the size of the rule table (complexity) and the length of solution paths it induces (optimality). Our optimization method uses hypervolume scalarization in conjunction with a Monte-Carlo backtracking search.

This work aims to design neural networks that can represent, and emulate execution of, traditional computer programs in symbolic languages. The networks can be trained on algorithmic tasks from scratch, or fine-tuned after "compiling" human-authored programs into initial weights. We have applied our method to algorithmic list processing tasks as well as robotics and automated planning algorithms. The basis of our technique is fast, gated associative weight changes, using a novel "store-erase" weight update rule that emulates (over)writing contents of random-access memory. Our analysis found that arbitrary, unlimited updates can be made while maintaining bounded weights (shown on the left for the 3D case) and correct emulation of random-access memory.

- Katz, G.E., Akshay, ., Davis, G.P., Gentili, R.J. and Reggia, J.A., 2021. Tunable Neural Encoding of a Symbolic Robotic Manipulation Algorithm. Frontiers in Neurorobotics, 15, p.744031. [paper] [code]
- Katz, G.E., Gupta, K. and Reggia, J.A., 2020, July. Reinforcement-based program induction in a neural virtual machine. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. [paper] [code]
- Katz, G.E., Davis, G.P., Gentili, R.J. and Reggia, J.A., 2019. A programmable neural virtual machine based on a fast store-erase learning rule. Neural Networks, 119, pp.10-30. [paper] [code]

This line of research involves full- and upper-body humanoid robots that can learn by imitating human teachers. We developed a framework called CERIL that uses

- Akshay ., Chen, X., He, B. and Katz, G.E., 2022. Towards Human-Like Learning Dynamics in a Simulated Humanoid Robot for Improved Human-Machine Teaming. In International Conference on Human-Computer Interaction (pp. 225-241). Springer, Cham. [paper] [code]
- Katz, G.E., Huang, D.W., Hauge, T., Gentili, R. and Reggia, J., 2017. A novel parsimonious cause-effect reasoning algorithm for robot imitation and plan recognition. IEEE Transactions on Cognitive and Developmental Systems, 10(2), pp.177-193. [paper] [code]
- Katz, G.E., Huang, D.W., Gentili, R. and Reggia, J., 2016, July. Imitation learning as cause-effect reasoning. In International Conference on Artificial General Intelligence (pp. 64-73). Springer, Cham. [paper] [code]

We have developed various numerical methods to better analyze and understand neural network activation and learning dynamics. For example, we introduced directional fibers (pictured on the left), mathematical objects that can be numerically traversed to enumerate many distinct solutions to systems of non-linear equations. They may be applied to enumerate fixed points of recurrent neural networks and other dynamical systems, or stationary points of objective functions. We also devised a predictor-corrector method to numerically traverse the loss level-sets of neural networks, in order to analyze the variation in regularization among parameter vectors with equal training loss.

- Tahir, N. and Katz, G.E., 2021, July. Numerical Exploration of Training Loss Level-Sets in Deep Neural Networks. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. [paper]
- Katz, G.E. and Reggia, J.A., 2018. Applications of Directional Fibers to Fixed Point Location and Non-convex Optimization. In Proceedings of the International Conference on Scientific Computing (CSC) (pp. 140-146). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). [paper] [code]
- Katz, G.E. and Reggia, J.A., 2017. Using directional fibers to locate fixed points of recurrent neural networks. IEEE transactions on neural networks and learning systems, 29(8), pp.3636-3646. [paper] [code]

In a previous research project we applied Bayesian inference techniques to cryo-electron micrographs of biological virus particles, to determine their 3D structure and understand their molecular machinery.

- Katz, G.E., Benkarroum, Y., Wei, H., Rice, W.J., Bucher, D., Alimova, A., Katz, A., Klukowska, J., Herman, G.T. and Gottlieb, P., 2014. Morphology of influenza B/Lee/40 determined by cryo-electron microscopy. PloS one, 9(2), p.e88288. [paper]
- Katz, G.E., Wei, H., Alimova, A., Katz, A., Morgan, D.G. and Gottlieb, P., 2012. Protein P7 of the cystovirus φ6 is located at the three-fold axis of the unexpanded procapsid. [paper]

*CIS 700: Special Topics*

PhD-level special topics courses, focused on reading, presenting, and reproducing research articles in a recent research area. Recent course topics include deep learning approaches to program representation, induction, and synthesis, and deep learning approaches to automated theorem proving.

*CIS 667: Introduction to Artificial Intelligence*

Graduate-level introductory course on Artificial Intelligence. Covers tree search algorithms (e.g., iterative deepening, A*, minimax); probabilistic modeling (e.g., maximum-likelihood estimation, expectation maximization, hidden Markov models), reinforcement learning (e.g. Markov decision processes, policy iteration, tabular temporal-difference Q-learning, policy gradient), basics of neural networks and gradient descent, and automated reasoning methods such as forward chaining, unification, and resolution.

*ECS102: Introduction to Computing*

Undergraduate course on introductory programming, using the Python language. Covers data types, literals, variables, control flow, libraries and packages, automated testing, and basics of object-oriented programming.