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: Large language models (LLMs) are indispensable tools that _accelerate_ biological data science and software engineering; however, LLMs are hammers that need to be placed in the hands of qualified computational biologists and bioinformatics engineers to render successful products and research output. LLM output needs to be verified and prompts need to be written by domain-specific expertise to elicit useful responses. On the engineering side, building deployable and maintanable systems requires understanding appropriate systems design/engineering best-practices because LLM "vibe-coding" trends toward bloated, difficult-to-maintain, and difficult-to-understand systems. People who partner with products expect the designers to conceptually understand and explain how the product works. To these ends, I integrate LLMs in my workflow with attention-to-detail paid toward rigorous testing, review, and upfront design.