resident curious cat and applied machine learning scientist @ ada cx

In my free time, I'm interested in how embodied experience leaves traces in language—and how to make those traces computationally legible. This encompasses all the things we humans know through our bodies before they reach words: sensory texture, movement quality, attentional orientation, the felt structure of an experience. I care about the formal and computational methods that make this kind of knowledge detectable and actionable to machines.

This sits at the intersection of 4E cognition, phenomenology, perception, Laban/Bartenieff Movement Analysis (LBMA) and cognitive/computational linguistics.

Also curious about second-order cybernetics, physical AI, and world models.

... and cinema; you can find my favourite films on Mubi.

sensorimotor-norms-metaphor Measuring how far metaphors travel across the body's dimensions. Applies Lancaster Sensorimotor Norms (Lynott et al., 2020) to the VU Amsterdam Metaphor Corpus to compute shift vectors and cosine distances between source and target domains—quantifying the sensorimotor reach of figurative language.

listening-experience-information-extraction This methodology enables the semi-automatic extraction of experiential domains (body, memory, place, social relations) by leveraging contextual information in documents. The findings show that perceptual descriptors do more than qualify sonic properties: they function as discursive anchors through which listening is mediated, apprehended through the active orientation of the subject toward what the sound affords for the regulation and modulation of lived experience.