RESEARCH

My research operates at the intersection of critical data studies, software auditing, and queer and media theory. I investigate how abstract, context-dependent cultural concepts—most centrally identity, toxicity, and vulnerability—are operationalized, classified, and flattened within datasets, models, generative AI architectures, and platforms.

During my doctoral research, I investigated how pre-generative computer vision models were trained to “see” and measure abstract cultural concepts like freedom, comfort, danger, and power. This work surfaced a fundamental computational tension: the structural requirement of machine learning to reduce multifaceted, subjective human phenomena into static, discrete mathematical variables. Today, I extend this critique into the inner workings of large language and multimodal models, analyzing how this flattening operates across their latent representations and downstream platforms.

Rather than approaching classification errors, representational harms, or hallucinations as isolated bugs to be patched, my methodology centers on what I have coined as “following the glitch.” Drawing on glitch feminism, queer failure, and critical AI studies, I treat the moments where computational taxonomies and binary schemas break down as diagnostic windows. These glitches reveal how systems operate underneath their interfaces, while pointing to tactical sites for queer refusal, epistemic opacity, and counter-hegemonic design.

Conceptual Web

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For the complete publication list, see the Publications page and/or Google Scholar.