I started my career in non-profits, many of which had limited resources to build information systems, so data was most often stored in one of three places: spreadsheets saved on someone's desktop locally, paper files locked away in a filing cabinet, or the memory of staff.
Trying to transform these ways of knowing into digital spaces, I focused on designing systems that amplify the work of staff and complement the expertise that practioners have built over years of doing their jobs. I also ran into the limits of data in education settings. I knew that what was being quantified systematically could become a compressed, flat representation of the work of staff and young people if used for funding, hiring, and decision-making in ways it was not designed for.
I became interested in understanding the limits of prediction and explanation using data. This drove me to join the Computational Social Science Lab, which unites computer science, statistics, and social science using digital data and platforms to study people.