Melissa McNeill
Melissa McNeill is a senior data scientist at the University of Chicago Crime Lab working to build and evaluate prediction models that are accurate, fair, and useful in the real world. She is a core contributor to Name Match, an open source probabilistic record linkage tool. Melissa holds a B.S. in Computer Science from Texas A&M and an M.S. in Analytics from Northwestern.
Sessions
Linking individuals across records or datasets is often a critical prerequisite for building useful data tools and answering interesting research or business questions. But doing it right is difficult and time-consuming, in part because current off-the-shelf tools do not provide a measure of linking accuracy and are too rigid to incorporate the user’s domain knowledge. In this talk, we’ll 1) define high-quality record linkage and discuss why it matters, 2) show how record linkage can be boiled down to a simple prediction problem, and 3) introduce Name Match, a new open source tool for customizable probabilistic record linkage.