The goal of the project is to investigate the mechanisms required to integrate recent advances from cognitive neuroscience, artificial intelligence, machine learning, data science, cybersecurity, and power engineering to augment power grid operators for better performance. Two key parameters influencing human performance from the dynamic attentional control (DAC) framework are working memory (WM) capacity, the ability to maintain information in the focus of attention, and cognitive flexibility (CF), the ability to use feedback to redirect decision making given fast changing system scenarios. The project will achieve its goals through analyzing WM and CF and performance of power grid operators during extreme events; augmenting cognitive performance through advanced machine learning based decision support tools and adaptive human-machine system; and developing theory-driven training simulators for advancing cognitive performance of human operators for enhanced grid resilience. We are building a new set of algorithms for data-driven event detection, anomaly flag processing, root cause analysis and decision support using Tree Augmented naive Bayesian Net (TAN) structure, Minimum Weighted Spanning Tree (MWST) using the Mutual Information (MI) metric, and unsupervised learning improved for online learning and decision making. In addition we use a discrete event model that captures the causal and temporal relationships between failure modes (causes) and discrepancies (effects) in a system, thereby modeling the failure cascades while taking into account propagation constraints imposed by operating modes, protection elements, and timing delays. This formalism is called Temporal Causal Diagram (TCD) and can model the effects of faults and protection mechanisms as well as incorporate fine-grain, physics-based diagnostics into an integrated, system-level diagnostics scheme. This project is in collaboration with Prof. Gautam Biswas from ISIS and Prof. Anurag Srivastava from Washington State University