Dr. Zhu received a Ph.D. in biostatistics from the University of Michigan in 2010 and then spent two years as a postdoctoral associate at the Department of Statistical Science and the Center for Human Genetics at Duke University. He joined the Biostatistics Branch of DCEG in 2012 as a tenure-track investigator. At DCEG, Dr. Zhu has developed novel statistical methods and computational packages to address scientifically important and statistically challenging research problems. In addition, he has led statistical analyses for multiple DCEG cancer genomic studies, as well as several national and international cancer genomic consortia.
Dr. Zhu integrates statistics and genomics to understand the etiology of mutational signatures and reveal tumor heterogeneity. He develops statistical methods/tools and leads the scientific investigations to:
- Extract mutational signatures and characterize their etiologies across different study designs and platforms, and
- Uncover inter- and intra-tumor heterogeneity with translational and clinical implications.
- REBET for a subregion-based burden test in rare-variant association studies.
- SubHMM to identify tumor subclones in next-generation sequencing studies.
- SKIT for a semiparametric kernel independence test when there are excess zeros.
- SUITOR to select the number of mutational signatures through cross-validation.
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