About

I am an M.S. Statistics student at the University of Washington with a focus on machine learning, unsupervised learning, and practical statistical modeling. My work sits at the intersection of research rigor and applied delivery: I enjoy building models and evaluation pipelines that are both scientifically defensible and useful in real product or research workflows. I am especially interested in representation learning and embeddings, including how to assess their reliability under different data conditions.

Alongside graduate study, I have contributed to applied ML efforts in industry and research settings, including experimentation, model evaluation, and communication of tradeoffs to diverse stakeholders. I am also motivated by biology and ML integration, where robust statistical thinking can improve how we model complex systems. I value reproducibility, readable code, and clear writing that helps teams make better decisions. Teaching and mentoring are core parts of my development, and I enjoy helping others build intuition for machine learning and data-driven reasoning.

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Open to applied scientist, ML engineer, and research collaboration opportunities.