Daniel C. Kilper
Papers
1
Total Citations
8
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1
About
Daniel C. Kilper is a leading researcher in optical communications and networking, with a focus on the intersection of physical-layer dynamics and machine learning. His work addresses critical challenges in fiber-optic systems, particularly the detection and classification of polarization events—a key issue for network reliability. In his highly cited 2020 paper, "Efficient Classification of Polarization Events Based on Field Measurements," Kilper pioneered the use of data augmentation combined with robot-generated fiber-disturbance data to classify rare polarization transients. By comparing machine learning methods for accuracy and training efficiency, he demonstrated how limited field data can be effectively leveraged for robust event detection. This contribution is vital for developing self-healing, intelligent optical networks. With over 8 citations on this work alone, Kilper’s research has influenced both academic and industrial approaches to network monitoring. His broader achievements include advancing energy-efficient optical systems and sustainable networking architectures, making him a key figure in bridging physical-layer science with practical, data-driven network management.
Research Focus
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