Summary
Xiaoyi Zhang is a PhD candidate in Biomedical Informatics at the University of Washington with seven years of experience building data-driven solutions for healthcare and population research. At the U.S. Department of Veterans Affairs they led the development of a national-scale data infrastructure used in a multi-year NIH/DoD survey and launched research using language models to surface gaps in care quality measurement. Their work at UW Medicine focuses on explainable ML for chronic disease management and on preserving model fidelity through complex enterprise data migrations. Earlier research at NYU explored cross-task transfer learning with BERT and applied deep learning to social media signals of socio-economic health risks. Xiaoyi blends rigorous applied research, production-grade data engineering, and a strong quantitative background (BS in Applied Math and BA in Chemistry) to translate clinical text and large-scale data into actionable insights. Colleagues describe their approach as both methodical and pragmatic—prioritizing interpretability and systems robustness in high-stakes healthcare settings.
7 years of coding experience
2 years of employment as a software developer
Master of Science - MS, Data Science, Master of Science - MS, Data Science at New York University
Bachelor's degree, B.S. in Applied Mathematics; B.A. in Chemistry, 3.85/4.00, Bachelor's degree, B.S. in Applied Mathematics; B.A. in Chemistry, 3.85/4.00 at Emory University
Doctor of Philosophy - PhD, Biomedical and Health Informatics, Doctor of Philosophy - PhD, Biomedical and Health Informatics at University of Washington - School of Medicine
High School Diploma, High School Diploma at Nanjing Foreign Language School
English, Chinese