Christopher Sprague is an AI researcher and engineer with 10+ years of experience marrying model-based reliability and learning-driven adaptability across robotics, generative models, and physical sciences. Currently a Member of Technical Staff at Orbital, he has led work on spatiotemporal emulators, geometric latent embeddings for diffusion/flow models, and trustworthy hybrid dynamical systems, including inventing a formal treatment of behavior trees. His background spans autonomous systems (AUV deployments and control theory), molecular generative models for drug discovery, and large-scale software engineering for open-source emulation tools. Comfortable working across US, UK, and EU contexts, he blends first-principles thinking with pragmatic engineering and active engagement in reproducible research and teaching. Notably, he has applied stability and symmetry principles to improve generative modelling and demonstrated practical multi-GPU benchmarking for ligand docking workflows.
10 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science Artificial Intelligence Robotics, Doctor of Philosophy - PhD Computer Science Artificial Intelligence Robotics at KTH Royal Institute of Technology
High School Diploma Physics, High School Diploma Physics at Oakton High School
Master of Science (M.S.) Aerospace Engineering Machine Learning, Master of Science (M.S.) Aerospace Engineering Machine Learning at Rensselaer Polytechnic Institute
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Christopher Sprague - Member Of Technical Staff at Orbital