Emily L. Spratt

Data Science Institute Research Fellow

Emily L. Spratt is a Post-Doctoral Research Fellow in the Data Science Institute in collaboration with the Historic Preservation Program in the Graduate School of Architecture, Planning and Preservation and the Department of Computer Science in the School of Engineering and Applied Science. Spratt is investigating the development of AI-enhanced technologies for the analysis, generation, and curation of art and architecture, the ethics surrounding this subject, and its philosophical implications and bearings. Spratt received her Ph.D. from Princeton University in the Department of Art and Archaeology. She also holds an M.A. in Renaissance art history from Princeton, an M.A. in Byzantine art history from the University of California, Los Angeles, and a B.A. in art history, religious studies, and psychology from Cornell University. Previously, Spratt taught in the Department of Art History and the Cultural Heritage and Preservation Studies program at Rutgers University, where she also was a member of the Art and AI Laboratory in the Department of Computer Science. Currently, she is a member of the Preservation Technology Laboratory and the Computer Vision Laboratory at Columbia.

Spratt has been the recipient of fellowships and awards including those from the Onassis Foundation, the Gladys Krieble Delmas Foundation, the Cini Foundation in Venice, the Cyprus American Archaeological Research Institution, the American Research Center in Sofia, Bulgaria, the Hellenic Ministry of Culture, the Frick Collection and Art Reference Library, and from the universities from where she holds degrees. She is on the digital and multimedia advisory board of the Renaissance Society of America, is a steering-committee member for The Frick Collection and Art Reference Library Scholars’ Advisory Group, is on the advisory board of the Artificial Intelligence Finance Institute of New York, and is the ethics advisor of Iconem. Spratt is also involved in sourcing, evaluating, and advising machine learning-related startup companies.

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