• The Zhou Lab at UMass Medical School

I am starting up my lab at UMass Medical School as a tenure-track Assistant Professor. Please visit my Lab website: http://chanzhoulab.org/

Postdoctoral Fellow, Graduate Students, and Visiting PhD students/Scholar positions are available: http://chanzhoulab.org/join-us

As a computational biologist and statistical geneticist, I have great interest in applying quantitative approaches into biomedical research to understand fundamental biomedical questions and advance human health. My research has been interdisciplinary in nature, including algorithm design, big data analyses, statistical/mathematical modeling, RNA biology, cell molecular biology, sequencing techniques, epigenetics and disease models. My major research goals lie in (1) computational/statistical methods development for analyzing large-scale high throughput sequencing data & heterogenous biomedical data; and (2) applying quantitative approaches to analyzing various big data towards understanding genomics and epigenomics for precision medicine.

My interdisciplinary background and more than 15 years’ research experiences have laid the foundation for me to lead my lab to achieve the aforementioned research goal. In recent years, I have developed computational methods, pipelines, and tools for analyzing high-throughput sequencing data, such as ChIP-seq, RNA-seq, GRO-seq and single cell sequencing data. I have successfully applied my computational methods to genome-widely understand the regulatory networks of long noncoding RNAs, circular RNAs, and epigenetics in human disease and development (Zhou et al., Genome Medicine, 2016; Zhou et al., Cell Reports, 2017; Daneshvar et al., Cell Reports, 2016; Batista et al., Cell Stem Cell, 2014). I have also closely collaborated with physicians, molecular biologists and immunologists by designing computational approaches and analyzing heterogeneous data for multiple projects in drug discovery, immunology, and disease for precision medicine (Motola et al., Hepatology, 2015; Mehta et al., Science Immunology, 2017; Chen et al., Scientific Reports, 2017).

My previous undergraduate and graduate training have prepared me with an excellent interdisciplinary background in computational biology, bioinformatics, and applied mathematics. In my education, I succeeded in becoming an independent researcher as early in college. As an undergraduate, I published a first-author and then again as a corresponding author paper in the journal “Annals of Combinatorics” in the field of biological mathematics (Zhou and Xie, Annals of Combinatorics, 2005). My PhD work focused on genome-wide identification and analyses of functional genomic elements and proteins in plant genomes using integrated computational approaches (Mao et al., BioEnergy Research, 2009; Zhou et al., Journal of Proteome Research, 2010; Zhou et al., Genomics, 2009). After finishing my Ph.D., I continued working at UGA as a computing specialist on computational algorithm development for phylo-genomics, and application in analyzing large-scale microbiome genomics data (Zhou et al, PloS One 2014).

Due to my highly collaborative experiences, I have a deep understanding of molecular biology, epigenetics, disease and medicine. Such training has made me be able to apply quantitative approaches to solve ‘multi-omics data mining’ problems and to design experiments. This interdisciplinary understanding of science places my lab at the forefront of the interface of quantitative biology, big data and biomedicine