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Erin Conlon
Associate Professor
Department of Mathematics and Statistics
Lederle Graduate Research Tower 1436
Amherst, MA 01003
Phone: (413) 5450622
Fax: (413) 5451801
Email: conlon _at_ math.umass.edu
http://www.math.umass.edu/~conlon
Education and Professional History
 Postdoctoral Fellow, Department of Statistics, Harvard University
 Visiting Scholar, Functional Genomics, Institute for Pure and Applied Mathematics (IPAM), University of California, Los Angeles
 Postdoctoral Fellow, Statistical Genetics, University of Washington, Seattle
 Ph.D. Biostatistics, University of Minnesota
 M.S. Biostatistics, University of Minnesota

B.S. Mathematics, University of Wisconsin, Madison
Research and Collaborators
Publications
 Data Science, Big Data and Analytics: I am currently developing Bayesian statistical methods for data science, big data and analytics, with the following researchers.
Xiaojing Wang
Zheng Wei
Alexey Miroshnikov
 Statistical Methods in Genomics and Bioinformatics: My research interests also include gene expression and DNA sequence
analysis, Bayesian models for the analysis of genomic data
and comparative genomics, with the following topics.
 Climate change and systems biology.
My current work focuses on climate change and systemsbiology approaches to the study of regulatory and metabolic networks of microbes, in collaboration with the lab of Kristen DeAngelis. You can read about our project here.
 Breast cancer gene expression studies.
I am also working on statistical and bioinformatic methods for breast cancer gene expression studies in humans with the lab of Joseph Jerry.
 Other projects involve the organisms
Prochlorococcus marinus, Geobacter, and Bacillus subtilis, in
collaboration with the labs of the following researchers.
Jeffrey Blanchard
Derek Lovley
Jeffrey Townsend
Richard Losick
Software
parallelMCMCcombine:
An R package for Bayesian methods for big data and analytics
Motif Regressor:
Regulatory motif discovery using gene expression information
Bayesian MetaAnalysis of Microarray Data
Teaching
Stat 516: Statistics II, Spring 2018
Stat 526: Design of Experiments, Spring 2018
Stat 697B: Bayesian Statistics, Fall 2017
