Xinyang Zhang
Papers
2
Total Citations
43
H-Index
2
About
Xinyang Zhang’s research bridges cutting-edge artificial intelligence with pressing environmental and biological challenges. His primary contributions lie in two distinct fields: deep learning for urban water infrastructure and comparative biomechanics of marine locomotion. In his highly cited 2023 work (40 citations), Zhang pioneered a deep learning framework for automated sewage pipe defect detection, directly addressing critical needs in urban water environment management. This innovation demonstrates how computer vision can transform infrastructure maintenance, reducing reliance on manual inspection. More recently, Zhang has explored the hydrodynamics of shark swimming, specifically examining how caudal fin morphology affects performance across three species—the banded houndshark, blue shark, and shortfin mako. Using kinematic models derived from video data, his 2025 study reveals how fin shape (sickle-shaped, asymmetrical crescent, and symmetrical crescent) correlates with swimming efficiency and maneuverability. This work has implications for both evolutionary biology and bio-inspired engineering. Zhang’s ability to apply sophisticated computational methods to diverse problems—from urban environmental systems to marine biology—showcases his versatility as a researcher. His work continues to influence sustainable infrastructure practices and our understanding of aquatic locomotion.
Research Focus
Key Achievements
Top Papers
- 1
- 2