a. Hau-Tieng’s homepage

From medicine, for medicine.

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


I am a 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
A recent media about a new venture: https://otc.duke.edu/news/pranaq-secures-3m-in-seed-funding/

Research Interest

applied harmonic analysis,
signal processing,
time-frequency analysis,
time-series 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) heterogeneous & multimodal physiological waveform signals, like ECG, EEG, PPG, BP, Resp, fMRI, or anything that has time as the main component like video. The target is extracting nonstationary dynamical information that complements static information commonly used in clinics for diagnosis, treatment and control.

I have intimately collaborated with physicians from various medical fields. Collaboration with any hospital is welcome. Please contact me directly via hauwu (at) math (dot) duke (dot) edu

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

Some of my research talks/slides

FFT 2022, Nonstationary time series analysis through landmark diffusion with clinical applications

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


2023 spring: STA542, Introduction to time series analysis (See Sakai)

2022 fall: MAT561, Numerical Linear Algebra (See Sakai)

2022 spring: STA611, Introduction to Statistical Methods (See Sakai)

2021 summer: lecture in Second International Summer School on Technologies and Signal Processing in Perinatal Medicine (TSPPM-2021) https://attend.ieee.org/tsppm-2021/ [Lecture slides available via request]

2021 summer: lecture in Mathematics for Nonstationary Signals and applications in Geophysics and other fields (NoSAG21) http://people.disim.univaq.it/~antonio.cicone/NoSAG21/Informations.html [Lecture slides available via request]

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

Almost forget who I am, luckily…