国际杰出计算科学家系列论坛

题  目：A Plea for adaptive data analysis

报告人：Norden E. Huang（黄锷），美国工程院院士，台湾“中研院”院士，中国工程院外籍院士

Research Center for Adaptive Data Analysis

Center for Dynamical Biomarker and Translational Medicine

National Central University

Zhongli, Taiwan, ROC

时  间： 6月28日（周五）下午2：45-3：45

地  点：中山大学数学楼104室

Abstract:

Data analysis is indispensable to every science and engineering endeavor, but it always plays the second fiddle to the subject area.  The existing methods of data analysis either the probability theory or the spectral analysis are all developed by mathematicians or based on their rigorous rules. In pursue of the rigorous, we are forced to make idealized assumptions and live in a pseudo-real linear and stationary world. But the world we live in is neither stationary nor linear.  For example, spectral analysis is synonymous with the Fourier based analysis.  As Fourier spectrum can only give meaningful interpretation to linear and stationary process, its application to data from nonlinear and nonstationary processes is problematical.  And probability distributions can only represent global properties, which imply homogeneity (or stationarity) in the population.  As scientific research getting increasingly sophistic, the inadequacy is become glaringly obvious.  The only alternative is to break away from these limitations; we should let data speak for themselves so that the results could reveal the full range of consequence of nonlinearity and nonstationarity.  To do so, we need new paradigm of data analysis methodology without a priori basis to fully accommodating the variations of the underlying driving mechanisms.  That is an adaptive data analysis method, based on the Empirical Mode Decomposition and Hilbert Spectral Analysis.  The result is present in a time-frequency-energy representation.  In fact, we can only define true frequency with adaptive method, which would lead to quantify nonstationarity and nonlinearity.  Examples from classic nonlinear system and recent climate will be used to illustrate the prowess of the new approach.

报告人简介：

Dr. Norden Huang is the K.T.Lee Chair Professor of National Central University. He held a doctoral degree (1967) in Fluid Mechanics and Mathematics from the Johns Hopkins University. In the past, he has been working on nonlinear random ocean waves. Recently, he has devoted all his time in data analysis, specifically in a new method, the Hilbert-Huang Transform, to process nonstationary and nonlinear time series. Over the last few years, he has applied this method to analyze data in the following areas: nonlinear ocean wave evolution data; earthquake signals and structure responses; bridge and structural health monitoring; biomedical signals such as blood pressure fluctuations; long term environmental data such as global temperature variations, Antarctic ice extents records, and solar irradiance variance; hydro-machinery design and machine vibration data. For this invention, he was awarded the 1998, 2003, 2004 NASA Special Space Act Awards. He was also the winner of the 1999 Federal Government Technical Leadership Award; the 2001 Federal Laboratory Development Award, 2006 Service to America Medal for Science and Environment, and, for his contribution in the filed of nonstationary and nonlinear data analysis, elected as a member of the National Academy of Engineering, 2000.

Dr. Huang serves as an Associate editor for Journal of Physical Oceanography, and Journal of Geophysical Research. He has published extensively on subjects covering data analysis method and its applications to natural science, engineering, biomedical and financial problems.

Currently, he is a K.T.Lee Chair Professor and the Director of the Research Center for Adaptive Data Analysis at the National Central University.

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　广东省计算科学重点实验室

　2013年6月25日