intro

Without the why,
there is no how.

M.S. Data Science at Columbia, with a background in financial economics. Economics is where I developed my systems thinking, a habit of tracing how a change propagates across a larger structure. The same instinct applies as much to building models as to understanding decisions. Data science is where I learned to test whether an effect is real, then build the tool it calls for. That experience is also why I trust a result only after I've investigated its foundation, and allow complexity only when simplicity is insufficient. Below: projects, and interactive explainers for the geometry behind concepts.