Briefly, I hold a PhD in plant, insect and microbial sciences, emphasizing in plant genetic and genomics from the University of Missouri-Columbia, and a Bachelor of Science in Agricultural Science from Truman State University in Kirksville, Missouri. I was introduced in the field of plant breeding and genetics in my first year in college, and was involved in plowing field with a tractor, planting seeds with hand, pollinating and collecting agronomic data from field trials, harvesting and statistically analyzing data.

While still pursuing my undergraduate career, I worked with diverse panel of maize germplasms, and developed gene specific markers to aid in the development of high amylose maize lines and to exploit the biodiversity of exotic maize inbred lines from Central and South America. Carrying my passion in plant genetics and big data analysis to my graduate school, I was involved in the development of two novel populations: near isogenic (NIL) and synthetic lines derived from domesticated corn and its wild relative, teosinte to elucidate the teosinte genetic diversity for agronomic and domestication traits in maize. While still in graduate school, I developed linear regression models to non-destructively estimate seed composition traits (total starch, protein, oil and individual fatty acids) in intact maize kernels and soybean seeds using near infrared reflectance spectroscopy (NIRS) and nuclear magnetic resonance (NMR) instruments.

The key findings from my PhD dissertation research were that the teosinte bears novel alleles for various plant and kernel architecture traits, which can be utilized for the improvement modern maize germplasm, and can provide unique source of variations to breeders and geneticist in further QTL and molecular studies, which will accelerate in the development of future elite maize germplasm. Currently, I am a postdoctoral computational biologist at Cornell University in VitisGen2, a multi-institutional project funded by the US Department of Agriculture-Agricultural Research