Faculty member at the Graduate school of Genome Sciences Technology

• Associate editor for the Journal of Biomedicine and Biotechnology
• Guest Associate editor for the Journal of Psychmetrika
• Reviewer for the National Institute of Health (NIH). 
• Reviewer for the National Science Foundation (NSF).
• Vice-President of the International Council on Biomedicine and Biotechnology
• see the link: http://www.i-council-biomed-biotech.org/
• Guest editor for the special issue of J. Biomed and Biotech.: Data Mining in Genomics and Proteomics.
• Program Committee organizer for the ACS/IEEE international Conference on Computer Systems and Applications (AICCSA-06)

Teaching:

• Stat 320: Regression analysis (Undergraduate level)
• Stat 583: Principal of Data mining using Statistical tools (Master level and MBA)
• Stat 578: Categorical Data Analysis (Master level)
• Stat 201: Business Statistics, concepts and applications (Undergraduate level)
• Stat 664: Advanced Inferential Statistics: Bayesian (PhD level)
• Stat 579: Multivariate data analysis (Master level)
• stat 574: Data mining and statistical tools for pattern recognition (Master and PhD level)

Research Interest:

• Data mining and knowledge discovery.
• Statistical tools for Genomics and Proteomics
• Bayesian analysis
• Clustering and model-based cluster analysis
• Mixture modeling for continuous, mixed data and imputed data
• Multidimensional scaling, Optimal scaling
• Classification and Neural Network.

Some Selected Publications:

• Bensmail, H., Buddana A., Semmes O. J. and Haoudi (2005) "A Functional Clustering Algorithm for High Dimensional Proteomics Data". Journal of Biomedicine and Biotechnology. In Press
• Wang, C. H., Kuo, W, and Bensmail, H. (2005). "Application of Image Processing Techniques and EM Algorithm to Detect Defect Patterns in Wafer Maps". IEEE transactions. Submitted
• Buddana, A, Bensmail, H and Ostrouchov, G (2005). Steering of Iterative Bayesian Clustering to Uncover Multiscale Structure in Massive Data Sets. Submitted to the Journal of Pattern recognition.
• Bensmail, H and Bozdogan, H (2004). Bayesian Clustering of Imputed and Mixed Data. Submitted to the Journal of Royal Statistical Society (JRSS).
• Liu Z., Dechang Chen, Bensmail, H and Ying Xu (2005). Gene Expression Data Clustering with Kernel Principal Component Analysis. Published in Journal of Bioinformatics and Computational Biology (JBCB).
• Liu Z., Chen D., Bensmail, H., Reifman, J. and Xu, Y (2005) "Gene Expression Data Classification with Kernel Principal Component Analysis." Journal of Biomedicine and Biotechnology (JBB). In press.
• Bensmail, H A, Semmens, J. and Haoudi, A. (2005). Bayesian Fast-Fourier Transform Based Clustering Method for Proteomics Data. Journal of Bioinformatics. In Press.
• Kwon, Y. and Bensmail, H. (2004). Bayesian autoregressive-threshold model for forecasting. Statistics department Technical report.
• Bensmail, H. Golek, H. M, Semmes, J. and Haoudi, A. (2004). Fourier-based bayesian clustering for proteomics data. Statistics department technical report, 2004. Download
• Bensmail, H. and J. Meulman, J.J (2003). Inferences for model-based cluster analysis with noise. Journal of Classification (20), page 49-76.
• Bensmail, H. and Haoudi, A. (2003). Post-Genomics and Proteomics Data analysis: J. Biomed. Biot., 4, 217-230. see://jbb.hindawi.com
• Bensmail, H., and Bozdogan, H. (2004). Adaptive Model-based Kernel Cluster analysis with optimal scaling. Submitted to JASA. Statistics department technical report
• Bensmail, H. Meulman, J.J (2000). Discriminant analysis with optimal scaling. Studies in Classification, Data Analysis, and Knowledge Organization. Springer-Verlag, page 60-67.
• Bensmail, H. Celeux, G. Raftery, A. & Robert, C (1997). Inference in model-based cluster analysis, Computing and Statistics, 1, N10, pp.1-10.
• Bensmail, H. & Celeux, G. (1996). Regularized discriminant analysis, Journal of the American Statistical Association (JASA), Vol. 91, No 436, pp. 1743-1748.

Statistical Software Produced: 

• KernDisc: Multivariate Kernel-Discriminant Analysis, University of Tennessee, SPLUS, 2001.
• KernMix: Multivariate Kernel mixture-model cluster analysis, University of Tennessee, SPLUS, 2002.
• Metrounfold: Bayesian Unfolding model via metropolis and Gibbs sampler in Data Theory group, 1999.
• EDDA: Eigenvalue Decomposition Discriminant Analysis (programmer) in R and S-Plus (INRIA) 1995.
• IMBCA1: Inference in Model-Based Cluster Analysis for linear, spherical and proportional covariance matrices in clustering, (Department of Statistics, University of Washington), 1996.
• PPCD: Prediction of Prostate Cancer Data (longitudinal data) using Gibbs Sampler, Fred Hutchinson Cancer Research Center, 1996. 

To read some articles on Clustering, visit this homepage

www.ece.northwestern.edu/~harsha/Clustering/clus.html

To view papers on Bayesian analysis, visit this homepage

http://www.bayesian.org/bayespeople.html

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