The Numerical Intelligent Systems Laboratory focuses on performing cutting-edge research into fundamental problems in artificial intelligence and machine learning from a numerical computation perspective. We are exploring problems in advanced knowledge representation, inference, and learning as it applies to system-level problems such as system monitoring and control, equipment health management, and precision agriculture. Techniques explored include probabilistic and Bayesian methods, evolutionary methods, and particle-based methods. We are also exploring problems in deep learning and explainable AI.