ABOUT

I am passionate about developing the next generation of microbial bioinformatics analyses. I am currently employed by Theiagen Genomics, where I collaborate with developers, analysts, and state public health labs to enable automated, cloud-based public health genomics. Beyond the computer, I strive to educate communities on fungal biology, which includes invited teaching and workshop presentations for technical expertise and general botany/mycology education. Please, view my GitHub portfolio, and reach out to discuss all things microbial genomics and mycology education.

My career includes 5 years in fungal natural product drug discovery analytical chemistry, 1 year in quality control chemistry, followed by 7 years in computational genomics (fungi, plants, prokaryotes, and viruses). My background has a strong focus on phylogenomics, general NGS data analysis (genomics/transcriptomics), collaborative software development, and hands-on mass spectrometry method development (LCMS/MS, GCMS/MS). In my free time, I tinker in all things from home repair, bouldering, to practical automation.

Statement on Artificial Intelligence in Biological Software Engineering: I am convinced that large language models (LLMs) are indispensable tools for biological data science and software engineering. However, LLMs are hammers that need to be placed in the hands of qualified computational biologists to reason, design, and quality control the outputs of LLM coding assistants. This is particularly important in biological data analysis, which differs from standard software engineering by requiring a large domain-specific knowledge base within biology alongside a robust software engineering skillset. To this end, I strive to integrate my boots-on-the-ground experience with programming langauges and biological data science to swiftly, iteratively design and quality assure the outputs of LLMs within my engineering and analysis work.