Education

2014 - 2016      Postdoctoral Training, University of Wyoming, Laramie, WY

2010 – 2014     PhD in Statistics, The University of Texas at Dallas, Richardson, TX

2008 – 2010     MS in Mathematics, concentration in Statistics, Texas Tech University, Lubbock, TX

2005 – 2007     BA in German (major), Mathematics (minor), North Central College, Naperville, IL

Research Interests

My current research interests are focused on the development of statistical methods for high dimensional  data analysis.  High dimensional data problems arise in various fields such as functional neuroimaging, DNA and metagenomic microbial communities sequencing.  I worked on fMRI voxel-wise connectivity analysis problems using wavelet shrinkage denoising methods and large covariance matrix estimation for dependent data.  My current research projects involve algorithmic and eigen-decomposition based dimension reduction techniques for next generation microbiome sequencing data visualization and health outcomes prediction. I also work on evaluating the effect of  data pre-processing bias on statistical analysis.

I collaborate with microbiologists, statisticians and mathematicians to achieve both biologically relevant and mathematically justified  solutions to these interdisciplinary problems.  As a part of our work, we provide software solution and data analysis methods manuals  to our collaborators working in clinical and translational research.

Publications

1. Leroux, A., Di, J., Smirnova, E., McGuffey, E., Cao, Q., Bayatmokhtari, E., Tabacu, L., Zipunnikov, V., Urbanek, J. K., and Crainiceanu, C. ”Organizing and analyzing the activity data in NHANES”, under review, 2017.

3.Thomas, J., Lutes, L., Smirnova, E., Das, B., Huzurbazar, S., Aldrich, L., Kepler, M. “Creation of Lifestyle Health-Related Self-Concept (Lifestyle-HRSC) Questionnaire", under review, 2017.

4. Jean, S., Huang, B., Brooks, J. P. , Edwards, D. J.,  Smirnova, E., Huzurbazar, S., Fettweis,  J. M., Serrano, M. G.,  Sheth, N. U.,  Strauss, J. F.  III,  Jefferson, K. K. , and Buck, G. A. "Multi-omic profiles discriminate characteristics of the vaginal environment in early pregnancy", under review, 2017.

5. Smirnova E.,  Huzurbazar S., and Jafari F. "PERFect: PERmutation Filtering test for microbiome data", to appear in Biostatistics,  2018.

6. Grimes, M.,  Gaiser, J.,  Cook, W., Hall, B., Rikova, K., Smirnova, E.,  Clark, N., Lachmann, A.,  Hornbeck, P., Ma'ayan, A., and Comb, M. "Using protein phosphorylation, acetylation, and  methylation to outline lung cancer signaling networks", to appear in Science Signaling,  2018.

7. Smirnova, E., Ivanescu, A., Bai, J., and Crainiceanu, C. "A practical guide to big data," to appear in Statistics and Probability Letters, 2018.

8.  Brooks, J. P., Chen, G., Diao, L., Edwards, D., Fettweis, J. M., Huzurbazar, S.,  Rakitin, A., Satten, G. A.,  Smirnova, E.,  Waks, Z., Wright, M. L., Yanover, C., Zhou, Y.-H. "Changes in Vaginal Community State Types Reflect Major Shifts in the Microbiome",  Microbial Ecology in Health & Disease, 28:1, 2017.

9. Cacho A., Smirnova E., Huzurbazar S., and Cui X. "A Comparison of Base-calling Algorithms for Illumina Sequencing Technology",  Briefings in Bioinformatics,  17:5, pp. 786-795, 2016.

10. Efromovich S., and  Smirnova E. "Statistical Analysis of Large Cross-Covariance and Cross-Correlation Matrices Produced by fMRI Images'', J Biom Biostat, 5:2, pp. 1- 8, 2014.

11. Efromovich S., and  Smirnova E. "Wavelet Estimation: Minimax Theory and Application'', Sri Lankan Journal of Applied Statistics, 5:4, pp. 17 - 32, 2014.