Title: Discrete Fourier Transform and Its Applications on Global Data Analysis

Abstract: Fourier transforms have been widely used in many areas in mathematics, statistics, and engineering. In this talk, I will first provide a review on the Fourier transform and its relationship with circulant/block circulant matrices, and present the applications of the Fourier transform in the analysis of global data, in particular, the study of axially symmetric processes on the sphere. More explicitly, I will discuss the consistency of commonly used covariance/variogram estimators on the sphere and present the use of Fourier transforms for data generation and spatial prediction when the underlying process on the sphere is axially symmetric.

Bio: Professor Haimeng Zhang received his Ph.D. degree in applied mathematics with
concentration on statistics from the University of Southern California. Currrently he is a
professor of Statistics in department of mathematics and statistics, University of North
Carolina at Greensboro. His research interests are Survival Analysis, Spatial Statistics and
 Applied Probability.