At USDA-ARS, I applied machine learning methods, including Bayesian Networks, to characterize antimicrobial resistance patterns in Salmonella and other bacteria. My research also analyzed whole-genome sequences to investigate source tracking and patterns associated with foodborne disease.
At the University of Florida, I studied the genetic and epigenetic regulation of complex traits in Populus deltoides. My dissertation used ATAC-seq to identify open chromatin regions and putative functional variants relevant to genomics-assisted breeding.
I developed bioinformatics pipelines for genomic data analysis, using Python, R, and Unix environments. My work included comparative genomics, variant analysis, and preparing data for machine learning applications.
Across research projects, I’ve worked with plant and microbial samples, from molecular lab techniques to computational analysis. I’ve also mentored students and supported laboratory members in developing bioinformatics pipelines.
