Stephen J. Verzi earned his PhD from the University of New Mexico in computer science, with a focus in neural network design and analysis. At Sandia National Laboratories, his professional experience includes algorithm development and computational modeling spans many application domains: artificial intelligence, machine learning, information retrieval and multilingual text analysis, neural function including the hippocampus and its substructures, system dynamics models of human behavior via the theory of planned behavior expressed using the mathematics of qualitative choice theory, individual-based and dynamical systems modeling, resilience metrics and their application in critical infrastructure, hospital evacuation and neural network dynamics, game-theoretic and agent-based models of adversarial behavior, and most recently exploring neural-inspired computation via phase-coding with application to image and video processing including anomaly detection as well as the application of adversarial reinforcement learning in grid resilience stability modeling. He helped develop and validate a population health effects model for policy-making.