I am a computational biologist who made the leap from the lab bench to the command line, drawn by the brain’s complexity and the sheer fun of making sense of chaos with code. My scientific home base is neuroscience, particularly piecing together the multi-layered data of neurodegeneration but you will also find me anywhere big, unruly biological datasets offer up a puzzle to solve.
While my professional path has taken me coast to coast, the common thread is a knack for unearthing hidden stories in data: cross-species comparisons, multi-layered omics, or connecting the dots in brain disease research. My work is equal parts detective and engineer: mulling over a specific bioinformatics challenge, refactoring code for modularity, and occasionally inventing solutions from scratch because sometimes, the answer doesn’t exist…yet.
One thing I have honed along the way is the skill (and enjoyment) of learning on the fly. I tackle new problems head-on, can ramp up quickly on unfamiliar tools or concepts, and believe that the best science (and best code) comes from an iterative loop of trying, tinkering, and improving.
Teaching and mentorship are where I get to pay this forward. As a Carpentries instructor and curriculum adviser, I get to help researchers take their first steps in R or Python, or finally vanquish that cryptic bash error together. There’s something deeply satisfying about watching confidence take root as new learners move from uncertainty to self-reliance. For me, the true reward isn’t in teaching a specific tool or a clever coding trick but in equipping people with adaptable skills and a mindset that lets them tackle new problems independently. Whether I am mentoring or building solutions myself, I focus on perspectives and approaches that will serve well beyond the next analysis or workshop.
What will you find here? Byte sized code, a mix of aha moments, how-to’s, and trivia that made me pause. If it helps you, that’s a win; if it helps a future me later, even better.