Virginia Tech® home

Leanna L. House

Associate Professor


  • Ph.D. in Statistics, Duke University, Durham, NC, 2006
    Diss: Non-parametric Bayesian Models in Expression Proteomic Applications
    Advr: Dr. Merlise Clyde and Dr. Robert Wolpert
  • M.S. in Statistics Duke University, Durham, NC, 2003
    Proj: Bayesian Identification of Differentially Expressed Genes
    Advr: Dr. Merlise Clyde
  • M.A.T. in Curriculum Development, Cornell University, Ithaca, NY, 1999
    Advr: Dr. Avery Soloman
  • B.S. in Biometry and Statistics, Cornell University, Ithaca, NY, 1998


  • American Statistical Association
  • International Society for Bayesian Analysis
  • Team-Professor for COS 2084: Integrated Science Curriculum
  • STAT 4214: Methods of Regression Analysis
  • STAT 4444: Applied Bayesian Statistics
  • STAT 5984: Hierarchical Models
  • Bayesian statistical modeling with an emphasis in model averaging, kernel regression, and Bayes linear
  • Uncertainty analysis of computer models/experiments
  • Data mining coupled with data visualization that promotes human-data interaction and education in Statistics
  • Applications in proteomics, bioinformatics, cosmology, climatology, and hydrology
  • Rougier, J., M. Goldstein, and House, L. (2012).Second-Order Exchangeability Analysis for Multi-Model Ensembles. Journal of the American Statistical Association. (To Appear)
  • Gudmestad, A., House, L., and Geeslin, K. L. (2012). What a Bayesian Analysis Can Do for SLA: New Tools for the Sociolinguistic Study of Subject Expression in L2 Spanish. Language Learning. (To Appear).
  • Leman S.C., House L., Maiti D., Endert A., and North C. (2013). Visual to Parametric Interaction (V2PI). PLoS ONE, 8(3): e50474. doi:10.1371/journal.pone.0050474.
  • Leman, S. and House, L. (2012). Improving Mr. Myagi’s Coaching Style: Teaching Data Analytics with Interactive Data Visualizations. Chance, 25, 4, 4-10.
  • House, L. (2011). An Application of Reification to a Rainfall -Runoff Model. Journal of Agriculture, Biological, and Environmental Statistics, 16, 4, 513-530.
  • Endert, A., Han, C., Maiti, D., House, L., Leman, S., and North, C. (2011). Observation level Interaction with Statistical Models for Visual Analytics. IEEE Symposium on Visual
    Analytics Science and Technology (VAST)
    , ISBN: 978-1-4673-0015-5, 121-130.
  • Tawfik, A. M., Szarka, J., House, L., and Rakha, H. (2011). Disaggregate Route Choice Models Based on Driver Learning Patterns and Network Experience. Intelligent Transportation Systems (ITSC), 14th International IEEE Conference, ISBN: 978-1-4577- 2198-4, 445-450.
  • House, L., Clyde, M., and Wolpert, R. (2011). Nonparametric Models for Peak Identification in MALDI-TOF Mass Spectroscopy. Annals of Applied Statistics, 5, 2B, 1488-1511.
  • Banks, D., House, L., and Killourhy, K. (2009). Cherry-Picking for Complex Data: Robust Structure Discovery. Philosophical Transactions of the Royal Society, Series A, 367, 4339-4359.
  • House, L., Clyde, M., Y. Huang (2005). Bayesian Identification of Differential Gene Expression Induced by Metals in Human Bronchial Epithelial Cells. Bayesian Analysis, 1.1, 105–120.
  • House, L. and Banks, D. (2004). Robust Multidimensional Scaling. Proceedings in Computational Statistics 2004, Physica-Verlag, Berlin, 251-260.
  • House, L., and Banks, D. (2004). Cherry-Picking as a Robustness Tool, in Classification, Cluster Analysis, and Data Mining, eds. Banks, House, Arabie, McMorris, and Gaul, Springer-Verlag, Berlin, pp. 197-208.
Leanna L. House

Associate Professor

404 Data and Decision Sciences 
727 Prices Fork Road 
Blacksburg, VA 

406-A Hutcheson Hall 
250 Drillfield Drive
Blacksburg, VA 

Leanna House's Webpage