Erin Conlon
Associate Professor
Statistics, biostatistics, bioinformatics, genomics, Bayesian statistics, statistical methods for data science and big data.
Current Research
My research areas focus on developing Bayesian statistical methods for genomics, bioinformatics, data science and big data. This includes integrating multiple sources of genomic information into statistical models, such as expression, DNA sequence and functional data. Other work involves determining Bayesian models for gene expression meta-analysis, and comparative genomics approaches to identifying genetic regulatory networks in prokaryotic species.
Learn more at www.math.umass.edu/~conlon
Academic Background
- BS Mathematics, University of Wisconsin, Madison
- PhD Biostatistics, University of Minnesota
- Postdoctoral training: University of Washington, Seattle
- Postdoctoral training: Harvard University
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.
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.
Miroshnikov, A., Wei, Z., Conlon, E.M. (2015) Parallel Markov chain Monte Carlo for non-Gaussian posterior distributions. Stat, 4, 304-319.
