Katherine M. Kinnaird is a computational researcher working at the intersection of machine learning, mathematics and cultural analytics. The central driving force behind her work is the building and supporting of authentic bridges between statistics, mathematics, machine learning and music information retrieval, as well as other disciplines like biology, human computer interaction and literature. Her research program builds a methodology for comparing high-dimensional sequential data that can be broadly applied to many questions from a range of fields, such as comparing musical songs.

At the center of Kinnaird’s work is the domain-neutral creation of a low-dimensional representation for sequential data streams, which allows for an objective comparison between subjective objects such as recordings of music. Kinnaird is also committed to supporting communities so that all who have interests in computer science, statistics and mathematics are encouraged, supported and inspired. She co-organized the 2013 Workshop for Women in Machine Learning (WIML), co-located with the largest machine learning conference in the world, Neural Information Processing Systems, and she has served on the WiML executive board, including a term as president. Kinnaird also was a co-organizer for the first Women in Music Information Retrieval (WiMIR) Workshop in 2018.