Naijun Sha

Office: Bell Hall 203
Phone: 915-747-6844
Fax: 915-747-6502
E-mail: nsha@utep.edu

Department of Mathematical Sciences
University of Texas at El Paso
500 W University Ave
El Paso, TX 79968-0514

Education

Research Interests

Classification and Clustering, Variable Selection Technique, Reliability, Bayesian Approach, Bioinformatics.

Courses (Spring 2012)

Honors and Awards

2005-2012, Elected Member, Marquis Who's Who in America.
Jan. 2004, Elected Member, Academic Keys Who's Who in Sciences Higher Education (WWSHE).
Apr. 2001, Phi Kappa Phi, Texas A&M University.

Select Publications

Savistsky, T., Vannucci, M. and Sha, N. (2011). Variable selection for nonparametric Gaussian process priors: models and computational strategies. Statistical Science, 26(1), 130-149.
Kwon, D., Tadesse, M.G., Sha, N., Pfeiffer, R. and Vannucci, M. (2007). Identifying biomarkers from mass spectrometry data with ordinal outcomes. Cancer Informatics, 3, 19-28.
Sha, N., Tadesse, M.G. and Vannucci, M. (2006). Bayesian variable selection for the analysis of microarray data with consored outcomes. Bioinformatics, 22(18), 2262-2268.
Tadesse, M.G., Sha, N., Kim, S. and Vannucci, M. (2006). Identification of biomarkers in classification and clustering of high-throughput data. In Bayesian Inference for Gene Expression and Proteomics, Kim-Anh Do, Peter Mueller and Marina Vannucci (Eds). Cambridge University Press, 97-115.
Tadesse, M., Sha, N. and Vannucci, M. (2005). Bayesian variable selection in clustering high-dimensional data. Journal of American Statistical Association, 100, 602-617.
Sha, N., Vannucci, M., Tadesse, M.G., Brown, P.J., Dragoni, I., Davies, N., Roberts, T.C., Contestabile, A., Salmon, M., Buckley, C. and Falciani, F. (2004). Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage. Biometrics, 60(3), 812-819.

Association Links

Useful Links