My academic training is highly interdisciplinary, integrating both pure and applied mathematics with engineering. I completed my master’s studies in mathematics at the University of California, Santa Barbara and Sichuan University, where my work spanned algebraic geometry and scientific computing. Prior to that, I earned my bachelor’s degree in Polymer Science and Engineering from the University of Science and Technology of China, which provided a strong foundation in materials science, modeling, and quantitative analysis. This cross-disciplinary background allows me to approach research problems from both theoretical and computational perspectives.
I am currently a researcher in the machine learning team of the ARPA-H branch of the MAGIC-SCAN project. My work focuses on developing and evaluating machine-learning-driven pipelines for large-scale biomedical and pathological data, with an emphasis on robust modeling, interpretability, and integration with domain-specific scientific workflows.
My research interests lie at the intersection of Topological Machine Learning, Computational Pathology, and Scientific Visualization. I am particularly interested in methods that combine topological and geometric insights with machine learning to improve model interpretability, reliability, and scientific insight, especially in data-intensive applications where structure, uncertainty, and explainability are critical.