Dr. Xiangfeng Wang’s team is working on development of novel computational methodologies to facilitate crop breeding, including genomic selection breeding model, molecular design breeding model and idealized genome design model. These models are implemented as an intelligent breeding decision system as a service platform for modern Big Data-assisted crop breeding. Different from previous methods, Dr. Wang’s team utilizes a variety of machine learning approaches to solve a series of geno-to-phenotype prediction problems.
He also works on understanding the genetic mechanisms underlying maize heterosis, and investigates how domestication and improvement genes/mutations are cooperatively selected and utilized in modern maize breeding, using large-scale F1 populations constructed under different genetic backgrounds. The mutations and genes identified from these populations will facilitate the development of molecular design breeding models for seed industry.
Professor, 2014 -
China Agricultural University
Assistant Professor, 2010 - 2014
University of Arizona
postdoc, 2008 - 2010
Postdoc, 2007 - 2008
Ph.D. in Bioinformatics, 2002 - 2007