Newton Mount Ida Campus: School of Design 125J

Erin Conlon develops Bayesian statistical methods for data science, big data and analytics. Her research interests also include gene expression and DNA sequence analysis, Bayesian models for the analysis of genomic data and comparative genomics. Application areas include climate change and breast cancer genomics.


Ph.D. University of Minnesota

M.S. University of Minnesota

B.S. University of Wisconsin—Madison


Statistics; Bayesian methods for data science, big data and analytics; bioinformatics; biostatistics.

Selected Publications

  • Wei, Z., Kim, D., Conlon, E.M. (2022). A Bayesian approach to the analysis of asymmetric association for two-way contingency tables. Computational Statistics, 37, 1311-1338. Journal link: https://link.springer.com/article/10.1007/s00180-021-01161-9.
  • Wei, Z., Conlon, E.M., Wang, T. (2021). Asymmetric dependence in the stochastic frontier model using skew normal copula. International Journal of Approximate Reasoning, 128, 56-68. Journal link: https://doi.org/10.1016/j.ijar.2020.10.011.
  • Wei, Z., Conlon, E.M. (2019). Parallel Markov chain Monte Carlo for Bayesian hierarchical models with big data, in two stages. Journal of Applied Statistics, 46, 1917-1936. Journal link: https://www.tandfonline.com/doi/full/10.1080/02664763.2019.1572723.
  • Gregory, K.J., Roberts, A.L., Conlon, E.M. et al. (2019). Gene expression signature of atypical breast hyperplasia and regulation by SFRP1. Breast Cancer Research, 21(1), 76. Journal link: https://breast-cancer-research.biomedcentral.com/articles/10.1186/s13058....
  • Pold, G., Conlon, E.M., Huntemann, M. et al. (2018). Genome sequence of Verrucomicrobium sp. strain GAS474, a novel bacterium isolated from soil. Genome Announcements, 6, e01451-17. DOI: https://doi.org/10.1128/genomeA.01451-17.
  • Wei, Z., Wang, X., Conlon, E.M. (2017). Parallel Markov chain Monte Carlo for Bayesian dynamic item response models in educational testing. Stat, 6, 420-433. Journal link: http://onlinelibrary.wiley.com/doi/10.1002/sta4.164/abstract.
  • Miroshnikov, A., Wei, Z., Conlon, E.M. (2015). Parallel Markov chain Monte Carlo for non-Gaussian posterior distributions. Stat, 4, 304-319. Journal link: http://onlinelibrary.wiley.com/doi/10.1002/sta4.97/abstract.
  • Miroshnikov, A., Conlon, E.M. (2014). parallelMCMCcombine: An R package for Bayesian methods for big data and analytics. PLOS ONE, 9(9), e108425. Open access link: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0108425.
  • Conlon, E.M., Postier, B.L., Methe, B.A., Nevin, K.P., Lovley, D.R. (2012). A Bayesian model for pooling gene expression studies that incorporates co-regulation information. PLOS ONE, 7(12), e52137.
  • Conlon, E.M., Song, J.J. (2012). Bayesian Computational Methods for Meta-Analysis of Gene Expression Studies. In, Probability: Interpretation, Theory and Applications, Y.S. Shmaliy ed., Nova Science Publishers, pp. 245-268.
  • Conlon, E.M., Postier, B.L., Methe, B.A., Nevin, K.P., Lovley, D.R. (2009). Hierarchical Bayesian meta-analysis models for cross-platform microarray studies. Journal of Applied Statistics, 36, 1067-1085.
  • Conlon, E.M. (2008). A Bayesian mixture model for metaanalysis of microarray studies. Functional and Integrative Genomics, 8, 43-53.
  • Conlon, E.M., Song, J.J., Liu, A. (2007). Bayesian meta-analysis models for microarray data: a comparative study. BMC Bioinformatics, 8, 80.
  • Conlon, E.M., Song, J.J., Liu, J.S. (2006). Bayesian models for pooling microarray studies with multiple sources of replications. BMC Bioinformatics, 7, 247.
  • Conlon, E.M., Eichenberger, P., Liu, J.S. (2004). Determining and analyzing differentially expressed genes from cDNA microarray experiments with complementary designs. Journal of Multivariate Analysis, 90, 1-18.
  • Conlon, E.M., Liu, X.S., Lieb, J.D., Liu, J.S. (2003). Integrating regulatory motif discovery and genome-wide expression analysis. Proceedings of the National Academy of Sciences USA, 100, 3339-3344.