معرفی
Hau-Tieng Wu is affiliated with Duke University, where he conducts research in applied mathematics and machine learning, with a focus on diffusion-based methods for nonstationary time series and biomedical signals.
His work centers on diffusion maps, manifold learning, and spatiotemporal analysis, particularly applied to hemodynamic monitoring via arterial blood pressure (ABP) signals. He has contributed to theoretical advances such as L^∞ spectral convergence and robustness to heterogeneous and colored noise in manifold learning frameworks.
While no specific publications or students are listed in the provided text, his research intersects with computational physiology, kernel methods, and geometric data analysis. He has presented his work in academic seminars, indicating active engagement in the applied math and data science communities.
No awards, grants, emails, or team information are available in the current data.
