Skills
Python, R, statistical modeling (logistic/linear regression, PCA, clustering, Random Forest), data visualization (ggplot2, Matplotlib), geospatial analysis (ArcGIS, GPS-based field mapping), ecological field methods (AIM protocol, avian point counts, forest inventory), bioinformatics (RNA-Seq, PRS analysis, proteomics, genome annotation), high-throughput data integration, population stratification correction, cross-validation, multicollinearity diagnostics, report generation (RMarkdown, knitr, broom), RESTful APIs, molecular biology techniques, data wrangling, Bash scripting, genomic tools, 3D protein modeling.
About
I’m a bioinformatician and environmental scientist with a passion for applying data-driven solutions to climate, conservation, and public health challenges. With a strong foundation in ecology and advanced training in bioinformatics, I specialize in analyzing complex biological and environmental datasets to uncover insights that guide sustainable decision-making.
My background includes graduate-level research on gene–environment interactions in heart failure using UK Biobank data, as well as field-based ecological studies on forest restoration and avian biodiversity. With a background in environmental science and bioinformatics, I build inclusive, collaborative teams and apply data science tools to challenges in climate, genetics, and conservation.
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