Duncan Eddy is an aerospace engineer and research-focused technologist with nine years of experience building cloud-native, automated mission operations and robust AI systems for space and safety-critical domains. He has led spacecraft operations and autonomy teams at Capella and Amazon Kuiper, delivering fully lights-out constellation management, rapid commissioning, and scalable collision-avoidance services. More recently he blends academic rigor and applied research as a Stanford postdoc and HAI Research Fellow, using reinforcement learning and failure-detection techniques to probe and harden AI-driven decision systems across autonomous vehicles, LLM safety, and critical infrastructure. Duncan combines deep astrodynamics and GNC expertise with software engineering best practices—CI/CD, containerized services, and high-reliability cloud architectures—to move research into production. He’s also driven organizational change, mentorship, and diversity in aerospace teams while shepherding regulatory compliance and operational partnerships with agencies like NOAA and the 18th Space Control Squadron. Notably, his PhD work produced open-source astrodynamics tools that have been validated against on-orbit data and adopted by industry for operational missions.
8 years of coding experience
6 years of employment as a software developer
Bachelor's Degree Mechanical Engineering, Bachelor's Degree Mechanical Engineering at Rice University
Doctor of Philosophy Aerospace Engineering, Doctor of Philosophy Aerospace Engineering at Stanford University
Contributions:27 releases, 1 review, 43 commits in 1 year 7 months
rustrust-wrappersofa
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