Applied ML, statistical rigor, clear delivery.

I am an M.S. Statistics student at the University of Washington focused on machine learning, unsupervised learning, and practical statistical modeling. My work sits between research rigor and applied delivery: models, evaluations, and explanations that are defensible enough for research and useful enough for product work.

I am especially interested in representation learning, embeddings, biology + ML, and how to assess model reliability under changing data conditions. I value reproducible experiments, readable code, and clear writing that helps teams make better decisions.