Yi He, MS Biostatistician II
E-mail:   yi.he@vanderbilt.edu Phone:  615.343.3878 Fax:    615.343.4924	Vanderbilt University School of Medicine Department of Biostatistics S-2323 Medical Center North Nashville, TN 37232-2158

Research Interests

• Bayesian Modeling
• Clinical Trial
• Medical Research

Education

      Ph.D. in Biostatistics expected in 2006
      School of Public Health, University of Minnesota
      Dissertation: Improved Markov Chain Monte Carlo Methods for Variance Components in Hierarchical Linear Models
      Advisor: Dr. James S. Hodges

      M.S. in Biostatistics 2004
      School of Public Health, University of Minnesota
      Thesis: Multivariate Normal Mixture Models in Cluster Analysis to Detect Gene Differential Expression from Microarray Experiments
      Advisor: Dr. Wei Pan

      M.S. in Applied Mathematics 2002
      School of Mathematics, University of Minnesota

Work Experience

      Biostatistician II 2005 - present
      Department of Biostatistics, Vanderbilt University
      Supervisor: Dr. Dean Billheimer

      Statistical Intern (statistical consultant) 2004, 2005
      Department of Biostatistics, Vanderbilt University
      Supervisor: Dr. Bonnie Lafleur

      Research Assistant 2002 - present
      Department of Biostatistics, the Univeristy of Minnesota
      Advisor: Dr. James S. Hodges

      Recitation Instructor 2000-2002
      School of Mathematics, University of Minnesota

Computer Skills

      SAS, R, Splus, C, JAVA, UNIX

Workshop

      Bayesian computing using BUGS language

Publications

He, Y., Pan, W. and Lin, J. (2005). Cluster Analysis Using Multivariate Normal Mixture Models to Detect Differential Gene Expression With Microarray Data. submitted.

He, Y. and Hodges, J.S., and Carlin, B.P. (2005) Re-considering the parameterization in Hierarchical models. submitted.

LaFleur, B. and He, Y. (2005) A likelihood-based approach to modeling real-time RT-PCR data with right censoring. To be submitted soon.

He, Y. and Hodges, J.H. (2005) A note on the posterior estimate for variance parameters in Hierarchical models. To be submitted soon.