
I am a father and a crop geneticist. I am fascinated by natural variations in my studies of rice panicle size and maize root architecture. As the Big Data, both the next generation sequencing (NGS) data in
genomics area and the high throughput phenotyping data explode incredibly today. Phenotyping the hidden half underground is a long-standing bottleneck to the crop root genetics study. Currently, my colleagues and I are setting up a few root phenotyping platforms to facilitate the root studies. Efforts making are on development of imaging technologies, computational infrastructure,
and statistical methods that can capture and analyze morphologically
complex networks over time and at high-throughput. Naturally, I am doing genetics with statistics, computer modeling (yes, R and Python!!!) and molecular tools. In details, my current project combines expertise in imaging (field shovelomics, optical 3D gel, X-ray CT, etc.),
computational analysis, and quantitative genetics (SNP mining, GWAS, Genomic Selection, and .) with molecular biology
to understand root growth plasticity under different nitrogen (N) levels. Final goal would be improving the nitrogen usage efficiency (NUE), especially the uptake efficiency (NUpE), by modification of root system architecture (RSA).