I am an independent researcher (BS math, MS neuroscience) interested in the structure of scientific reasoning and how experimental knowledge is represented and communicated. My work focuses on questions at the intersection of scientific methodology, formal systems, and tools for understanding research.

My background spans scientific research, mathematical and conceptual modeling, neuroscience, networks, topological data analysis, ML and the study of how complex systems of knowledge are organized. I am particularly interested in approaches that combine formal mathematical structure and semantics with practical tools for understanding scientific literature.

Currently, I am developing Hypothesis Prism, a framework that attempts to represent hypothesis-driven experiments as structured graphs rather than purely textual narratives. More broadly, my goal is to explore new ways of representing scientific knowledge that could improve clarity, reproducibility, and the ability to reason about research across disciplines in the light of AI scaling.

I am especially interested in work that sits between traditional academic research and practical tool-building—projects that attempt to rethink how scientific knowledge is structured, interpreted, and shared.