I recived my Ph.D in Statistics from Michigan State University in June 2002 under the direction of Hira L. Koul. After graduation, I joined in the Department of Mathematics and Statistics at University of New Hampshire. I was promoted to an associate professor in 2008 and professor in 2015. I have taught courses at UNH, such as, Asymptotics of Statistics, Nonparametric Smoothing, Time Series Analysis, etc.


My research areas mainly include semi- and nonparametric regression analysis with dependent errors, nonparametric smoothing methods and asymptotics of statistics. In particular, any unknown curve or image (e.g., density function, regression function, etc.) can be approximated by a series with finite terms with respect to a basis (e.g., wavelets, splines, Fourier, orthogonal series, etc). Once those coefficients among above series are properly estimated, one obtains a semi- or nonparametric estimate for the curve. Its optimal properties (e.g., convergence rates, mean integrated squared error, etc.) could be derived using thresholding or nonthresholding method, because many of those coefficients are close to zero (sparsity of signals or images). This method could be applied to many other statistical contexts, such as time series analysis for spectral density estimation and its hypothesis testing, adaptive hypothesis testing for non- or semi-parametric models, signal approximation and compresses sensing, etc.