I am currently a Founding Engineer at a [STEALTH STARTUP] where I predominately develop cognitive-inspired ML systems. Our research predominantly revolves around Representation Learning, Novel ML Algorithms, and Mechanistic Interpretability.
My research Interests lie in Neuro-Symbolic AI, specifically in cognitive-inspired systems that learn continuously from few examples, grow their own concept hierarchies the way people do, and stay explainable and interpretable at every layer.
I also enjoy deploying Edge AI to see how far we can push local compute to aid those in the field.
A deep latent-variable approach that optimizes a large latent hierarchy to automatically discover taxonomic structure and prototype clusters directly from unlabeled data; statistical optimization walking a gradient space toward emergent topology.
Frames taxonomic networks as the shared representation that pairs connectionist learning with symbolic structure: top-down symbol instantiation and discrete knowledge routing over a learned abstraction tree that stays inspectable.
Tests whether GPT-4o aligns with human typicality norms across five languages, probing the synchronization of statistical neural maps with the structured, human-like conceptual frameworks they must mirror.
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