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Dr. Fang Yu

Faculty Highlight - Fang Yu, PhD, UNMC College of Public Health

Faculty Highlight – Fang Yu, PhD, Assistant Professor in the Department of Biostatistics

Dr. Fang Yu’s research focuses on the development of Bayesian statistical methods for analyzing high-throughput data. Bayesian statistical methods incorporate prior information to calculate the probability that a hypothesis may be true. One of Dr. Yu’s recently published manuscripts proposed a hierarchical Bayesian method to identify differentially expressed genes under multiple biological conditions. Her hierarchical Bayesian method fits parallel models on the studied genes and provides better model fit by allowing the genes to borrow information from other genes. Recent advances in molecular and computational biology have led to the development of powerful, high-throughput methods that allow researchers to study thousands of genes or protein expressions simultaneously. Massive high-throughput data sets have been generated in many fields of medicine and public health. Although these tools offer rich biological information, they are highly error prone, since many genes or proteins are monitored at the same time with a relatively small sample size. It is important, yet challenging, to efficiently analyze high-throughput data to gain insight into biological mechanisms.

Dr. Yu has obtained extramural funding in collaboration with UNMC investigators as co-PI and statistician. She brings her statistics knowledge and expertise in high-throughput data analysis to these research projects to provide solid statistical support on design, analysis, and interpretation of the data. Her collaborative work with UNMC investigators has been published in 15 peer-reviewed journal articles.

Dr. Yu obtained her MS in mathematics from the University of Massachusetts and her PhD in statistics from the University of Connecticut. She teaches two concentration courses for the COPH MPH Program: Biostatistics II (CPH650) and Correlated Data Analysis (CPH655). Dr. Yu also serves as an editor-in-chief for the International Chinese Statistical Association (ICSA) Bulletin, an official publication of the ICSA, distributed to over 1,000 ICSA members worldwide.

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