Lucy Havens

IASH Affiliate 2023-24
Lucy Havens

Lucy was born in Seattle, Washington, USA and moved to Edinburgh in 2017 for postgraduate studies at the University of Edinburgh.  She has pursued interdisciplinary academic degrees that combine computer science and business, design and data science, and natural language processing and archival science.  As a Master’s student, Lucy was awarded the Edinburgh College of Art Award for her dissertation titled 'Physically Encoding Collection Metadata'.  For her PhD thesis, 'Recalibrating Machine Learning for Social Biases: A Case Study of Gender Bias in Archival Documentation', Lucy has investigated the capabilities and limitations of text classification models for identifying types of gender bias in archival metadata descriptions.  She has published and presented her work at computational linguistics, digital humanities, and human-computer interaction conferences.

Lucy’s past work experience includes positions of research software engineer, web developer and designer, graphic designer, and business and technology consultant.  Lucy has worked in libraries, energy and utilities, retail, private equity, and finance.  She has a passion for interdisciplinary and collaborative work, particularly for bringing approaches from cultural heritage, the humanities, and the arts to computational research and work.  Past clients and collaborators include the Alan Turing Institute, British Library, National Library of Scotland, National Trust for Scotland, Royal Botanic Gardens Edinburgh, St Cecilia’s Hall and Musical Instrument Museum, and the University of Edinburgh's Library & University Collections.  Lucy also holds affiliations with the Centre for Data, Culture & Society and the Centre for Technomoral Futures, and is an Andrew Carnegie Society Scholar and member of the US’s most prestigious honors society, Phi Beta Kappa.  Her portfolio and publications can be found at lucyhavens.com.

I am a designer, researcher, and data scientist.  I create physical interfaces with tangible materials, digital interfaces with code, and hybrid (physical + digital) interfaces.  Through my work, I explore ways to make what is invisible or ignored, visible.  As a designer, I use speculative, data-driven, and human-centered methods to inspire my design and art practice.  As a researcher and data scientist, my work lies at the intersection of natural language processing, human-data interaction, visualization, and cultural heritage.