Contractor at CDC in the Next Generation Sequencing Core Lab (NGSCL) of the Division of Viral Diseases (DVD).- Benchmarking viral genome analysis pipelines for Oxford Nanopore Technology long read sequencing.- Developing nextflow pipeline for viral genome assembly, variant calling and phylogenetic analsyis.- Collaborating with data managers and microbiologists in assessing various genome analysis tools and pipelines.

Bioinformatics Research Associate (Postdoctoral scholar) working on the impact of variants on human genetic diseases.- Developed software to identify human disease‑associated genes based on orthology and phenology with model organisms.- Developed Bayesian inference models to predict between organisms gene associations.- Implemented bioinformatics pipelines in high‑performing computing environments (Condor and GoogleCloud).- Automated, tested and vetted comparative genomics pipelines.- Analyzed and correlated SNP, clinical and demographic human data from the All of Us genome database.

- Managed a database of 2,000+ whole‑genome sequences from newly collected yeast (Candida albicans) from different environments and locations.- Implemented bioinformatics tools and pipelines in high‑performance computing clusters (SLURM and TORQUE).- Automated data analysis from microbiology, genetics and molecular biology experiments.- Determined genetic variation in genes associated with antifungal tolerance and resistance across 2,000+ sequences.- Identified and annotate genetic variants in experimentally evolved lines of fungal pathogens- Mentored graduate and undergraduate students in bioinformatics and population genomics projects.

- Compiled, organized and managed a database of 2,000+ public short‑read yeast (Saccharomyces cerevisiae)genome sequences from environmentally and geographically diverse strains, including metadata.- Implemented bioinformatics tools and pipelines in high‑performance computing clusters (SLURM and TORQUE).- Automated genome assembly, base calling, structural variation detection, and phylogenetics.- Identified genetic variants statistically associated with aneuploidy in yeast using Genome‑Wide Association models.- Predicted aneuploidy prevalence in yeast through statistical models, phylogenetics and population genomics.- Detected and simulated contamination in whole‑genome data and evaluated its impact in genome analyses.- Benchmarked computational tools to detect ploidy, aneuploidy, and heterozygosity from whole‑genome data.- Mentored graduate and undergraduate students in bioinformatics and population genomics projects