Tsung-Hung Yao

Tsung-Hung Yao

Ph.d. Candidate of Biostatistics Department

University of Michigan

Biography

Tsung-Hung Yao is a Ph.D. candidate of Biostatistics Department at the University of Michigan under the advisory of Dr. Veerabhadran Baladandayuthapani and Dr. Zhenke Wu. His research interests are mainly in Bayesian modeling and high-dimensional data. This includes different methods such as Bayesian nonparametric, diffusion tree modeling, functional data analysis, and Bayesian graphical models. These methods are mainly motivated by the various applications such as the pre-clinical patient derived xenograft and multiomics integration data.

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Interests
  • Bayesian Modeling
  • Bayesian Nonparametrics
  • Functional Data Analysis
  • Diffusion Tree Modeling
  • Bayesian Hierarchical Clustering
Education

  • Ph.D. Candidate in Biostatistics, 2018

    University of Michigan

  • MSc in Biostatistics, 2017

    University of Michigan

  • BSc in Chemistry, 2013

    National Taiwan University

Research Interests

January 2022
Probabilistic Learning of Treatment Trees in Cancer
Probabilistic Learning of Treatment Trees in Cancer

In this paper, we propose a novel Bayesian probabilistic tree-based framework, which motivates a new metric of mechanistic similarity between two or more treatments.

Recent Publications

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Tsung-Hung Yao, Zhenke Wu, Karthik Bharath, Jinju Li, Veerabhadran Baladandayuthapan (2022). Probabilistic Learning of Treatment Trees in Cancer.

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Megan Lane, Graham C. Ives, Emily C. Sluiter, Jennifer F. Waljee, Tsung-Hung Yao, Hsou Mei Hu, William M. Kuzon (2018). Trends in Gender-affirming Surgery in Insured Patients in the United States. Plastic and Reconstructive Surgery – Global Open.

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