(thesis advisor)Marisa Jahn
May 21 2019
What is the aesthetic and analytical potential for machine learning to reveal semantic or visual patterns from large photographic archives? What are the challenges and caveats of working with machine learning in the archival context, where naturally anthropological, historical, and cultural aspects come into play? And finally, once patterns are identified, how can they compliment or shed light on research around shifting values for representation of self and family in colonial and then post-colonial contexts in the East?
After Image is an art installation comprised of a video piece and creative data visualization both showcasing a series of experiments done with an archive of the artist’s own family photographs in relation to a larger archive of South Indian studio photographs from the Tamil Studies in Studio Archives and Society (S.T.A.R.S). Each of these experiments employs machine learning and computational techniques to sort, average, and analyze the images in order to surface semantic and visual patterns across the hundreds of images. Through the use of these techniques, the artist performatively questions the notion of collective and individual identity, and highlight the complexity of the image as a data point.
Aarati Akkapeddi. After Image: A computational poem between the subject, the camera, technology, and representation.
May 21 2019. Parsons School of Design MFA Design and Technology program theses; 2019. New School Archives and Special Collections Digital Archive
. Web. 17 Sep 2019
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