Erik Ljungberg

I am an interdisciplinary scholar working at the intersection of Science and Technology Studies (STS), Environmental Humanities, and semiotics. My work challenges the traditional view that digital data provides a representation of world. Instead, I use a post-representational approach to understand algorithmic mediation. My research often moves fluidly between high theory and hard engineering; I like deep theoretical frameworks like Peircean semiotics, actor-network theory, Agential Realism, Modes of Existence, but I also like investigating circuit diagrams, code libraries, and statistical equations. I am driven to understand not only what data practices mean, but what they do. Notions of agency, action, and mediation are central to my research.

Currently, I ground these inquiries in my thesis on the historical and semiotic evolution of forest monitoring infrastructures—ranging from the manual labor of the Swedish National Forest Inventory to the algorithmic "view from above" of global satellite remote sensing, where I examine how these systems do not merely represent the forest as a static object, but function as vast semiotic engines that grow operative chains of mediation. I focus especially on the historical adoption of machine learning—from simple clustering algorithms to advanced neural nets—in these infrastructures. Ultimately, what matters to me is demystifying the black box of technology. I want to expose the political and material consequences of engineering decisions to ensure we understand the machinery that generates our knowledge of the natural world.

I will defend my thesis in Spring of 2026.