Leanna L. House
Students, please take note:
- 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
- Aug. 2012-Present, Member of Virginia Tech, College of Science Curriculum Committee
- Aug. 2012-Present, Virginia Tech Undergraduate advisor in Statistics
- Aug. 2012-Present, Head of Virginia Tech, Statistics Undergraduate Committee
- June 2012-Present, Junior lead of committee to develop a new undergraduate degree for the Virginia Tech, College of Science
- June 2011-Present, Serve on committee to develop an Integrated Science Curriculum (ISC) for undergraduate students at Virginia Tech
- Aug. 2009-Present, Coordinator for Women in Statistics Events at Virginia Tech
- Oct. 2010-Mar. 2011, Served on review panel for the 2011 international conference on Artificial Intelligence and Statistics (AISTATS)
- Aug. 2008-May 2010, Co-coordinator for Virginia Tech Colloquia Series
- Reviewed for JASA, Bayesian Analysis, Bioinformatics, Environmetrics, Technometrics, the Journal of Statistics Education
- 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.