a. Hau-Tieng’s homepage

From medicine, for medicine.

Hau-Tieng Wu, M.D., Ph.D. (吳浩榳)128017_wu_0032

I am an associate professor in Department of Mathematics and Department of Statistical Science at Duke University.

I am from Kaohsiung, Taiwan.

An old media report about me: https://today.duke.edu/2017/10/hau-tieng-wu-vital-signs

Research Interest

applied harmonic analysis,
signal processing,
time-frequency analysis,
machine learning,
manifold learning,
high-dimensional statistics,
high-frequency physiological data,

The main application is medical data. I have interest in various kinds of medical datasets, mainly high frequency (and/or ultra-long) physiological waveform signals, like ECG, EEG, PPG, BP, Resp, fMRI, or anything that has time as the main component (medical signal analysis). The target is extracting nonstationary dynamical information from (multimodal) time series that complements static information commonly used in clinics for diagnosis, treatment and control.

I have intimately collaborated with physicians from various medical fields, including cardiology, pulmonology, gynecology/obstetrics, anesthesiology, neurology, urology, etc…

I run MISTA (Medical information and signal, theory and application) lab in Duke university. See the website (under construction) for more information.

Collaboration from any hospital is welcome. Please contact me directly via hauwu (at) math (dot) duke (dot) edu

Some of my research talks/slides (out-of-date)

FFT 2017, De-shape short time Fourier transform, wave-shape manifold, and medical applications

Applied math seminar, Yale University, 12/4, 2017.
Theoretical properties of locally linear embedding and Dirichlet boundary


2021 spring: on dean’s leave.

2020 fall: MAT561, Numerical Linear Algebra (See Sakai)

2020 spring: STA250, Intro to Mathematical Statistics (See Sakai)

2020 spring: STA790, Time series analysis via time-frequency and complex analysis (See Sakai)

2019 fall: MAT531, analysis (See Sakai)

2019 spring: STA230, probability (See Sakai)

2018 fall: MAT531, analysis (See Sakai)

2018 spring: STA790, Geometric Statistical Learning

Lecture note 1: Summary of spectral graph theory and spectral embedding (Download)
Lecture note 2: Various spectral embedding algorithms (Download)
Lecture note 3: Kernel methods and RKHS (Download)
Lecture note 4: Background of differential geometry and Large deviation (Download)
Lecture note 5: Large deviation and asymptotical analysis (Download)
Lecture note 6: Clustering and its statistical analysis (Download)
Lecture note 7: Sensor fusion, dynamical system analysis (Download)
Lecture note 8: Representation and diffusion with the group structure (Download)
Lecture note 9: More algorithms and analysis, like t-SNE and random matrix (Download)

2017 fall: MAT230, probability (See Sakai)


Ph.D., Mathematics, Princeton University, 2006-2011

Thesis: Adaptive analysis of complex data sets [Download]
Advisor: Ingrid Daubechies

M.D., National Yang-Ming University, 1996-2003