Shuo Huang

University of New Orleans, Michigan Technological University

Papers

Total Citations

H-Index

About

Shuo Huang is a researcher whose work bridges two distinct domains: robotic surgery safety and mobile sensor networks. In the clinical realm, Huang’s most cited paper (2019, 21 citations) addresses a critical risk in transaxillary robotic surgery—brachial plexus injury from arm positioning. By investigating the use of somatosensory evoked potential (SSEP) for intraoperative nerve monitoring, Huang provided a practical framework to prevent neurological damage, directly impacting patient safety in minimally invasive procedures. This contribution highlights a keen focus on translating engineering principles into surgical safeguards. Earlier in their career, Huang explored foundational problems in robotics and sensing. Their work on compressive mobile sensing (2009, 5 citations) introduced a novel method for reconstructing sparse sensing fields using mobile robots, leveraging compressive sensing theory to reduce measurement burdens. This was extended in adaptive sampling research (2011, 2 citations), where Huang proposed feedback-driven algorithms to intelligently direct robotic sensors toward informative regions. These contributions, though smaller in citation count, demonstrate innovative thinking in resource-efficient environmental mapping—a precursor to modern active perception systems. Huang’s trajectory from theoretical robotics to applied surgical monitoring reflects a versatile researcher committed to solving real-world problems through interdisciplinary insight.