SEUNG JUN SHIN

Contact Information

e-mail: sshin [at] ncsu [dot] edu

Office: 4219 SAS hall

Department of Statistics

North Carolina State University

4219 SAS hall

Raleigh, NC 27695-8203

Biography

My hometown is a beautiful city of Busan in South Korea, which is the 2nd largest city in the country. After graduating high school, I left my hometown and family for advanced education and earned my Bachelor and Master degrees in statistics in Korea University, Seoul, Korea. Then worked at a research assistant position in the same institute until NCSU allowed me to join the Ph.D. program. Finally in 2008 left my country, and family again, to join at statistics department in NCSU for my Ph.D. degree.

Now, I am currently working on machine learning area for my dissertation under the guidance of Drs. Yichao Wu and Hao Helen Zhang. More specifically, the topic is about the two-dimensional, continuous, piecewise-linear and complete solution surface in some kernel-based models under the regularization framework, such as the (weighted) support vector machine and the kernel quantile regression; and their applications. My general research interests also include (both Bayesian and frequentist) nonparametric function estimation, variable selection, computer intensive approaches like bootstrap and Monte Carlo method, robust statistics, empirical process as well as statistical learning.

Detailed CV (pdf) as well as a full graduate course list (pdf) are available.

Submitted Papers

Shin, S.J. Wu, Y. and, Zhang, H.H. (2012) , Two-Dimensional Solution Surface for Weighted Support Vector Machines, submitted.

Shin, S.J. and, Ghosh, S.K. (2012) A Comparison study of the Estimation of the Maximum Tolerated Dose, submitted.(NCSU stat. dep. tech-report # 2638)

Publications

Jhun, M. and Shin, S.J. (2009) Bootstrapping Spatial Median for Location Problems, Communications in Statistics: Simulation and Computation, 38, 2123-2133.

Jhun, M. and Shin, S.J. (2007) Comparison Study for Missing Imputation Methods: a Focus on Canonical Discriminant Analysis, Journal of Korean Data Analysis (written in Korean), vol.9, no.2, p.673-685.

latest update at January 2012.