KAUST Ph.D. student Yuxiao Li has been selected by the Eastern North American Region (ENAR) of the International Biometric Society to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting.
Li will travel to Atlanta, Georgia, U.S., in March to receive his award at the beginning of the ENAR President's Invited Address Session and to attend the 2018 ENAR Spring Meeting.
Li, who is currently pursuing a Ph.D. in statistics under the supervision of Assistant Professor Ying Sun, received the award for his paper entitled "Efficient Estimation for Non-stationary Spatial Covariance Functions with Application to Climate Model Emulation." Li explained that in statistical environmental studies, there is a tension between the convenience and relative computational ease of using stationary assumptions and the more realistic conditions of non-stationary assumptions.
Li's paper proposes a new estimation procedure for certain types of non-stationary Matérn covariance functions making non-stationary estimations more efficient with the option of using the local stationary model as a special case. Li applied his model to North American precipitation data and explained that he was able to "perform high-resolution simulations and generate non-stationary precipitation fields on a finer scale."
This award holds a special place for Li, as it was the first research project he conducted at KAUST. He noted, "This award gives me confidence for future research and encourages me to pursue a promising academic career."