Yunshan Duan
Johns Hopkins, yduan25@jh.edu
Welcome to my homepage! I am a postdoctoral fellow in the Department of Applied Mathematics and Statistics at Johns Hopkins University, where I work under the supervision of Dr. Yanxun Xu. I completed my PhD in Statistics and Data Science at the University of Texas at Austin supervised by Dr. Peter Müller. Prior to UT Austin, I earned a Bachelor’s degree in Math from Fudan University in China.
Research Interests
My research develops statistical machine learning methods to make complex biomedical data analysis more interpretable, trustworthy, and decision relevant. I work at the intersection of statistics, machine learning, and biomedical science, pursuing interdisciplinary research that connects statistical theory and computation with important biomedical questions. Specifically, I am interested in
Methodology:
- Statistical machine learning: self-supervised representation learning; Gaussian processes; generalized Bayes; variational inference.
- Bayesian nonparametrics: dependent random partitions; finite mixture model; model-based clustering.
- Clinical trial methods: Bayesian adaptive trial designs; sequential decision-making; information borrowing; surrogate endpoint evaluation.
Applications:
- Biomedicine: electronic health records (EHR); longitudinal medication data; HIV; mental health and cognition.
- Genomics: single-cell data; spatial transcriptomics; multi-omics integration.
- Clinical trials: innovative early-phase trial designs; surrogate endpoints; real-world data.