Speakers: Mao Li, PhD

Mao Li, PhD


Senior Research Scientist, Data Science Facility
Donald Danforth Plant Science Center


Mao Li is a mathematician who specializes in plant science. She is a Senior Research Scientist at Data Science Facility and a new PI at the Donald Danforth Plant Science Center. She got her PhD in biomathematics at Florida State University. Then she joined Topp Lab as a Postdoc Associate at the Danforth Center. Her research mainly focuses on quantitative data analysis, development of mathematical methods such as persistent homology based approaches, and computational algorithms to quantify plant morphology, below and above ground, from microbe to the globe.

Color Pattern Quantitative and Aesthetic Analysis in Coleus Leaves

Nowadays, advanced imaging technologies allow us to generate vast amounts of image data every day. While 3D phenomics are rising in popularity, 2D imaging is still most accessible method. We are making efforts on extracting information as much as possible and fully utilizing the data. Among many, how to quantify complex color pattern remains challenge. In this talk, I will talk about a quantitative color analysis framework using 2D Coleus leaf as an example. Coleus is a popular ornamental plant that exhibits a diverse array of foliar color patterns. New cultivars are currently hand selected by both amateur and experienced plant breeders. We collected 2D images from one of the largest coleus breeding populations in the world and quantified pigmentation patterns. We then analyzed relationships between maternal plants and their progeny, identified features that underlie breeder-selections, and collected and compared consumer input on trait preferences. This approach enable comprehensive exploration of complex color patterning and has broad applications.