Summary
William Chapman is a Senior Member of Technical Staff at Sandia National Laboratories with 11 years of experience bridging computational neuroscience, neuromorphic hardware, and hardware-aware AI. He leads funded projects that translate biologically inspired algorithms into energy-efficient deployments for spatiotemporal prediction, reinforcement learning, and graph neural networks. His background spans academia and national labs, where he developed state-of-the-art temporal prediction architectures, explainable sensor fusion models, and petascale forecasting methods for chaotic systems. Known for combining software, analog hardware design, and rigorous experimentation, he routinely mentors junior researchers and secures external funding to advance applied research. Based in Albuquerque, he brings a rare mix of hands-on algorithm development and practical hardware constraint optimization rooted in a PhD in Neuroscience.
12 years of coding experience
6 years of employment as a software developer
Master of Arts - MA, Computational Cognitive Neuroscience, Master of Arts - MA, Computational Cognitive Neuroscience at University of Colorado Boulder
Doctor of Philosophy - PhD, Neuroscience, Doctor of Philosophy - PhD, Neuroscience at Boston University