Female reproductive development occurs in the ovarian follicle, which contains a germ cell, the oocyte, surrounded by granulosa and theca cells. Signaling between the oocyte and granulosa cells is necessary to develop a fertile oocyte. Our research focuses on understanding the molecular and functional interactions within the ovarian follicle that promote proper germ cell development. This knowledge can then be used to develop better therapies for infertility, contraception, or species preservation and improve reproductive management in animal agriculture.• Managed the cloud storage of imaging data, deployed a MongoDB database, generated interactive data visualizations, and performed statistical analyses to screen for potential contraceptive targets from a drug library of over 14,000 drugs. • Integrated and analyzed Methyl-, ChIP-, and RNA-sequencing data that identified novel regulatory mechanisms in the non-coding region of ribosomal DNA during early embryogenesis.• Identified 386 differentially regulated genes using microarray data extracted from chicken ovaries that may contribute to the reduced fertility observed in excess feeding practices.• Applied a linear-model based approach to RNA-sequencing data sets from female germ cells of four species which identified 481 genes whose expression were conserved and 2,226 whose expression diverged by speciation in the oocyte development process.

The proper conservation and natural regeneration of native tree species in urban forests includes careful consideration of potential losses to large herbivores and soil contamination by regional anthropogenic sources.• Employed advanced elastic net regularization, bootstrapping, and cross-validation methods to identify the soil elements that contribute to the abundance of seven native tree species in urban forests.• Aided in experimental and sampling design to best assess survival and growth of xeric species to promote urban natural forest regeneration.• Estimated a 20% increase of sapling populations and 34% expansion of native trees after white-tail deer exclusion from an urban forest through generalized linear mixed model analyses.

Assisted with grading, student questions, data collection, and coding support in Python, R, and SAS for graduate-level statistics courses.STAT 415: Introduction to Mathematical StatisticsTopics include: point and error estimation, linear regression, ANOVA, non-parametric tests.STAT 505: Applied Multivariate Statistical AnalysisTopics include: repeated measures, MANOVA, discriminant analysis, PCA, factor analysis, canonical correlations, clustering methods.STAT 508: Applied Data Mining and Statistical LearningTopics include: exploratory data analysis, regression methods, subset selection and shrinkage methods, dimension reduction methods, discriminant analysis, decision trees, SVM, clustering methods.STAT 510: Applied Time Series AnalysisTopics include: ARIMA models, forecasting, smoothing and decomposition methods, intervention analysis, ARCH models, spectral analysis.

Our goal at the Data Learning Center is to support faculty, staff, post-doctoral scholars, and students on all phases of the data life cycle to produce reproducible and transparent research, gain insights from data, and share re-useable data. • Advised clients of diverse backgrounds on study design and statistical methods using software including MATLAB, Minitab, JMP, R, SAS, and SPSS.• Designed, moderated, and led workshops on data reproducibility and statistical analysis in R for over 100 faculty, staff, and graduate students. • Created guides for commonly requested statistical analyses in R, Minitab, and JMP and published a GitHub Pages website to host guides.