Vicky Yao, Ph.D., is an assistant professor in the Department of Computer Science at Rice University. She was recruited to Rice University with the help of a CPRIT Recruitment of First-Time, Tenure-Track Faculty Members award. Dr. Yao earned a Ph.D. in the Department of Computer Science from Princeton University (2018) and she earned a M.S. in Statistics from the University of Chicago (2011). She was a postdoctoral fellow at the Lewis-Sigler Institute for Integrative Genomics at Princeton University.

Dr. Yao’s research focus is in computational biology, with a particular interest in developing statistical and machine learning approaches to model the underlying molecular processes of disease through large scale integration of high-throughput -omics data.

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Vicky Yao, Ph.D., is an assistant professor in the Department of Computer Science at Rice University. She was recruited to Rice University with the help of a CPRIT Recruitment of First-Time, Tenure-Track Faculty Members award. Dr. Yao earned a Ph.D. in the Department of Computer Science from Princeton University (2018) and she earned a M.S. in Statistics from the University of Chicago (2011). She was a postdoctoral fellow at the Lewis-Sigler Institute for Integrative Genomics at Princeton University.

Dr. Yao’s research focus is in computational biology, with a particular interest in developing statistical and machine learning approaches to model the underlying molecular processes of disease through large scale integration of high-throughput -omics data. Large scale data integration methods can help tackle several critical questions: how to bridge the gap between model organisms (e.g., laboratory mouse) and the human disease, how tissue and cell type specificity may underlie tumor heterogeneity, and how splicing changes may rewire interaction networks. She has worked on modeling tissue and cell type specificity as well as disease progression, both by developing general methods (such as semi-supervised network integration) and in applying them to decipher the molecular underpinnings of diseases such as Alzheimer’s, Parkinson’s, and rheumatoid arthritis. An important facet of her research is building intuitive, interactive systems as interfaces to the models and predictions that she develops and she has built such systems whenever appropriate.

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