Nobody wants a pipeline. They want the answer at the end of it.
I'm a bioinformatics engineer on the data and development team at the Bove Lab in UCSF's Department of Neurology(opens in a new tab). The lab studies multiple sclerosis at the intersection of digital and precision medicine and sex and gender in neurology. Its research combines data from wearables, phones, and remote assessments with questions about how hormones and reproductive exposures shape inflammation and repair. I build the systems underneath that work, turning device streams, clinical records, and research data into tools and datasets the team can trust.
I tend to follow data all the way from source to decision. That might begin with an instrument in a clinic, a wearable, a phone, or a database of records. The destination might be an analysis or the dashboard a neurologist opens during a visit. That dashboard includes a map of local MS resources I helped build and years of gait and assessment videos arranged so a clinician can see how a patient's movement has changed. When the person represented by the data may be sitting in the room, quality stops being abstract. The same is true of extracting a structured measure from thousands of narrative notes: it can turn a question that was too expensive to ask into one a researcher can answer this quarter.
The assignment changes constantly. One week it is a cohort nobody has assembled before; the next, a figure for a grant due Friday. The underlying problems are familiar to any data team: sources that disagree, volumes that break the obvious approach, and results that still need to reproduce a year later. Owning systems over the long term has also taught me that the handoff matters as much as the build. I write the glossary, the runbook, and the decision record because a system nobody else can operate isn't finished.
I took an indirect route into software. Before UCSF, I spent three years as a chemist at the EPA's freshwater toxicology laboratory in Duluth, Minnesota, now home to the Great Lakes Toxicology and Ecology Division(opens in a new tab). The work moved between analytical chemistry, cell biology, animal studies, and the field. I prepared and measured chemical stocks for fish-tank exposures, ran LC-MS analyses, cell assays, and radioimmunoassays, and deployed fish in natural waters before retrieving, dissecting, and processing them. It gave me a literal source-to-result view of research: every measurement began with a chemical I prepared, an instrument I ran, or a fish I handled.
I wrote very little code there, but one VBA macro replaced an afternoon of copying and pasting in Excel with a keystroke. That feeling stuck. I went on to study software engineering at 42 Silicon Valley and never returned to the bench. Across both fields, I've co-authored 26 peer-reviewed papers, and my work has been cited over 450 times(opens in a new tab).