when i joined the data intelligence lab, i had never worked in a research environment before. i expected to spend most of my time training models, but i quickly realized how much research depends on good software. before anyone can experiment with machine learning, someone has to collect the data, organize it, clean it, and make sure everyone is working from the same foundation. that’s where i ended up spending most of my time.
what i worked on
most of my work focused on building software for researchers, not on the models themselves. i worked with noaa solar observations, helped automate data pipelines, and built solarflare-labeler, an open-source python package that prepares datasets for machine learning. a lot of the actual work was less glamorous than that sounds: reproducibility, documentation, testing, and making sure a script i wrote once could still be trusted six months later.
what surprised me
i always imagined research as people training models and reading papers. i didn’t realize how much engineering happens before any model exists. building software other people could actually trust turned out to matter just as much as building anything accurate.
what i’m taking with me
this experience made me realize i like building the tools other people build on top of, more than i like the finished product itself. working in the lab also changed how i saw the department. instead of only seeing professors in a classroom, i was suddenly collaborating with researchers and seeing how academic work actually happens behind the scenes.