Xinyuan Zhang

Xinyuan Zhang completed her undergraduate coursework from the University of Sydney, where she double majored in Finance and Statistics and researched sentiment analysis in the Computing Finance Lab. Xinyuan received an MS in Statistics from UCLA – her thesis explored preference models for two-sided platforms. Her research lies at the intersection of computational methods and human-centered AI, with applications in e-commerce and digital health. I develop theory-guided artifacts that span the information value chain: from holistic tensor-based representations of digital trace data, to multimodal AI-assisted decision support systems. Her work draws on design science, Bayesian methods, and large language models, and aims to make AI systems more interpretable, actionable, and impactful.