Kyle Guan

Nokia (United States), BAE Systems (Sweden)

Papers

Total Citations

H-Index

About

Kyle Guan’s research bridges the critical intersection of optical communications, distributed sensing, and machine learning, with a focus on enhancing the reliability and intelligence of networked systems. In his most cited work, “Efficient Classification of Polarization Events Based on Field Measurements” (2020, 8 citations), Guan pioneers rare-event classification of polarization transients using data augmentation combined with robot-generated fiber-disturbance data. This study systematically compares machine learning methods, offering insights into accuracy and training efficiency—a vital contribution for resilient optical networks. Earlier, in “Distributed sensing and communications in tactical robotic networks” (2008, 5 citations), he developed a distributed algorithm that leverages proactive robotic mobility to balance sensing coverage and communication performance. This work addresses foundational challenges in self-configuration and mobility control for autonomous networks. Guan’s research demonstrates a unique ability to integrate field measurements with advanced computational techniques, advancing both theoretical understanding and practical deployment. His contributions are particularly impactful for applications in defense, infrastructure monitoring, and next-generation communication systems, where robust, adaptive sensing and classification are paramount.