Sophia Chefalo
Massachusetts Institute of Technology
Sophia Chefalo is an artist and researcher who reframes photography as a technique of preservation rather than a technology defined by its output. Working across deep time, archival material, and organic matter, extracting the logic of photographic thinking from the camera itself, asking what fixation, survey, and compression make possible when separated from the apparatus that has historically delivered them. Currently pursuing a Master of Science in Art, Culture, and Technology at MIT (2024-2026), Chefalo has been recognized through prestigious awards, including being longlisted for the 2026 and 2025 Aesthetica Art Prize, receiving the MIT Architecture Departmental Fellowship, and earning the Scholastic Art and Writing National Silver Medal for Photography Portfolio.
Articles of Sophia Chefalo
The Non-Deterministic Apparatus, From Index To Icon
With a recent Samsung Galaxy Smartphone, one can no longer photograph the real moon. Instead, when trying to do so, the result contains details that were never optically present. How does one navigate the ontological shift of images that have photographic authority, yet display an increased displacement of indexicality? This essay follows the introduction of computational supplementation, the replacement of the shutter via text prompts, and machine learning models that average singular traces into statistical multitudes. The new boundaries of the post-photographic universe are self-referential, post-corporeal, post-light icons that only intensify the question of how to read images.
After Technical Images: Towards a Theory of Post-Technical Imaging
While Flusser’s concept of technical images remains foundational for understanding modern image-making, its limitations become evident when applied to emerging visual culture, which operates beyond immediate human sensory capabilities and constraints of previous apparatus. This essay proposes the concept of corporeal imaging as an alternative system that classifies imaging as relational to the body. This taxonomy enables the incorporation of contemporary imaging practices such as networked astronomical observations and generative AI systems that supersede traditional single-body relations. These developments necessitate new theoretical approaches for understanding image making beyond technical imaging.
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