I work on machine learning systems for scientific and technical problems where structure matters.
My current focus is on graph-based models, agentic workflows, and reproducible ML pipelines, with particular emphasis on biological and molecular systems. I’m interested in approaches that scale in understanding, not just in compute.
This site is a place to document the direction of that work, the systems I’m building, and the ideas that guide it.
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