Young Kinh-Nhue Truong

University of North Carolina at Chapel Hill

Young Kinh-Nhue Truong, Ph.D., is a professor of Biostatistics at the University of North Carolina at Chapel Hill. His research expertise includes statistical learning, functional modeling, time series, Spatio-temporal data analysis, and event history analysis. He has contributed significantly in the areas of statistical time series/longitudinal modeling using splines, window or kernel-based smoothing methods, and wavelets. His current research focuses mainly on Spatio-temporal data analysis with the aim to spatially localize dynamic processes in the functional magnetic resonance imaging (fMRI) or electroencephalography (EEG) human brain data. He is also interested in developing statistical inference for group comparison based on human brain imaging data and methods for analyzing neuro-spike train data. His teaching and research experience in linear mixed modeling has provided new insights and approaches to many statistical analyses for handling between- and within-subject variability.

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Latest work with IntechOpen by Young Kinh-Nhue Truong

Splines provide a significant tool for the design of computationally economical curves and surfaces for the construction of various objects like automobiles, ship hulls, airplane fuselages and wings, propeller blades, shoe insoles, bottles, etc. It also contributes in the description of geological, physical, statistical, and even medical phenomena. Spline methods have proven to be indispensable in a variety of modern industries, including computer vision, robotics, signal and image processing, visualization, textile, graphic designs, and even media. This book aims to provide a valuable source on splines and their applications. It focuses on collecting and disseminating information in various disciplines including computer-aided geometric design, computer graphics, data visualization, data fitting, power systems, clinical and epidemiologic studies, disease detection, regression curves, social media, and biological studies. The book is useful for researchers, scientists, practitioners, and many others who seek state-of-the-art techniques and applications using splines. It is also useful for undergraduate senior students as well as graduate students in the areas of computer science, engineering, health science, statistics, and mathematics. Each chapter also provides useful information on software developments and their extensions.

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