PhD candidate Information + STS
University of Michigan
My project considers how earth scientists, drone technologists, environmental activists, and policymakers acquire and classify image and spatial data on deforestation in Indonesia. These teams of experts hope that new digital techniques, from algorithmic detection of forest fires to automated flight routes, can help resolve the longstanding problem of image recognition: the biased nature of forest classification. Centering on how human bias is defined and “corrected” with algorithmic techniques, I explore how these teams observe signs of forest change that are otherwise poised as difficult to detect with an untrained human eye.
I craft models and art installations to make theory. Some of them were completed in collaborations with DIY maker and fabrication labs, DoIIIT and DIYbioSG. I am part of Making Sensory Ethnography, a workshop at University of Michigan that
convenes practice and method around the prefix sensory, examining how it expands, challenges, or align with ethnographic engagements.