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About

Ziyou Wu is a researcher working at the intersection of robotics, dynamical systems, and data-driven modeling, with contributions spanning both theoretical and applied domains. Wu's most influential work addresses fundamental challenges in Dynamic Mode Decomposition (DMD), a powerful mathematical framework for extracting spatial and temporal patterns from high-dimensional time series data. This 2021 paper has garnered 61 citations, reflecting its significance to researchers across fluid mechanics, robotics, and neuroscience who rely on DMD as an analytical tool. Beyond data-driven methods, Wu has made meaningful contributions to multi-legged robot locomotion, tackling the long-standing challenge of accurately modeling robots with six or more legs under realistic conditions involving slipping. Despite such robot designs offering superior stability and maneuverability, they have remained underutilized largely due to modeling difficulties — a gap Wu's work directly addresses through rigorous experimental validation. Complementing this, Wu's earlier investigation into Coulomb friction crawling systems challenges conventional assumptions, demonstrating a counterintuitive linear relationship between force and velocity in friction-dominated locomotion. Together, Wu's body of work bridges fundamental mechanics with practical robotics, offering tools and insights that support the next generation of robotic systems and data analysis methodologies.