I’m most interested in the work between a promising model and a dependable system. I move from problem framing, modeling, and evaluation into the data and deployment decisions that make machine learning useful in production.
I work through constraints early and in the open, partnering with product, engineering, and domain collaborators to understand tradeoffs, reduce surprises, and deliver systems people can trust.