Dry Lab
Most of my passion lives in tech, hacking, digital privacy, and digital security but I'd be lying if I ignored my roots as a student of bioinformatics (a "bioinformatician"?). The crossroads of computation and genetics is getting more attention by the day in the AI age, and it carries enormous potential to reshape our lives (for better or for worse... looking at you, COVID). I've been fortunate enough to earn ASCP credentials across Microbiology, Cytogenetics, Molecular Genetics, and Bioinformatics (a long story I'm happy to tell you anytime you've got a spare hour… or forty), so I know enough to hold a real conversation here. I'm fully on board with the idea that technology, AI, and genetics are three of the defining industries of the future and I'm here for it.
A "dry lab" is where the computation happens; the counterpart to the wet-lab bench. These write-ups sit at that intersection: reproducible pipelines, data integrity, and the kind of root-cause troubleshooting that looks the same whether you're chasing a skewed microarray or a misbehaving production system. Same rigor, different substrate. No fluff, no JavaScript, no tracking