Tyler Cody

Papers

Total Citations

H-Index

About

Tyler Cody is a leading researcher at the intersection of machine learning and systems engineering, with a primary focus on the operational test and evaluation (T&E) of ML-enabled autonomous systems. His work addresses the critical challenge of standardizing communication protocols for embedded ML applications in robots, satellites, and unmanned vehicles. Cody's major contribution lies in extending the IEEE Standard for Automatic Test Markup Language (ATML) to accommodate machine learning systems, a foundational step toward reliable and interoperable testing frameworks. His most-cited paper, "On Extending the Automatic Test Markup Language (ATML) for Machine Learning" (2024), has garnered early attention with 3 citations, signaling growing impact in this emerging field. By tackling the urgent need for messaging standards in edge ML, Cody's research bridges the gap between traditional test engineering and modern AI deployment. His work is particularly notable for its practical implications in defense and aerospace applications, where rigorous T&E is essential for safety and mission assurance. As the field of operational ML testing continues to mature, Cody's contributions are poised to shape industry standards and enable the reliable integration of intelligent systems into critical infrastructure.