Engineering the forefront of pathogen genomics

At Theiagen Genomics, I translate pathogen genomics to actionable inference for our government and private partners by working across a team of engineers and analysts. I drive collaborative bioinformatics pipeline development, cloud job deployment, automated analysis, and AI-integration for tailored biological analysis across all domains of life.

In the LLM era, I integrate my conceptual understanding of biology and software engineering background with the increased speed of contemporary development. I began at the bench, spending 6 years culturing microbes and experimenting in fungal natural product drug discovery analytical chemistry. Since 2018, I have transitioned my hands-on biological experience into dry-lab computational genomics. My expertise is now broadly across domains of life, having worked directly with bacteria in my post-doctoral research, and further to viral analysis at Theiagen. My methodological background has a strong emphasis on phylogenomics, general NGS data analysis (genomics/transcriptomics), collaborative software development, and hands-on mass spectrometry method development. Take a look around at my resume and software portfolio.

AI in biological software engineering

Large language models (LLMs) are indispensable tools that accelerate biological data science and software engineering; however, LLMs are hammers bounded by limited context: they are oracles that will produce the prompted path, and therefore need to be guided by qualified biologists and software engineers to render successful products and research output. I integrate LLMs in my workflow with attention-to-detail paid toward upfront Spec-driven development; establishing essential testing conditions as guardrails for agentic coding; and conducting rigorous human review. To this end, I employ scheduled agents, self-made skills for redundant tasks, and limit prompting context to discrete tasks to increase the quality of LLM output.