Research

Project 1: Harnessing Machine Learning to Enhance Plant Health through Microbiome Analysis

Our research focuses on developing a cutting-edge machine-learning approach to identify key microbiota combinations that boost plant health in nutrient-poor soils. Using common milkweed as our model species, we are collecting and analyzing data across Virginia, including plant health traits, root microbiome composition (via metagenomic sequencing), soil type, nutrient content, and other environmental factors. This data will fuel our machine-learning model, revealing novel plant-microbiome interactions that enhance plant growth in challenging soil conditions.

Project 2: Exploring the Genetic Origins of Floral Patterning in monkeyflowers (Mimulus)

Our research investigates how genetic interactions lead to new and complex traits, focusing on the intricate floral pigmentation patterns in Mimulus. While species like Mimulus cupreus and Mimulus luteus var. variegatus produce solid-colored flowers, hybridization between them creates striking patterns.

Using genomic analysis and mathematical modeling, including Turing instabilities, we explore how their genomes interact to produce new traits. This work sheds light on plant color pattern evolution and enhances our understanding of how genetic diversity and mathematical principles drive novel characteristics in nature.

Genetics and Sustainability of Traditional Taro Farming 

Our lab studies the population genetics of taro and how cultivation methods impact plant and soil microbiomes. In collaboration with anthropologists, we focus on traditional taro farming on Rurutu in French Polynesia. This project explores the sustainability of these ancient practices and their effects on genetic diversity and the soil microbiome, comparing traditional and modern methods. This project, by integrating biological and cultural perspectives, we aim to support sustainable agriculture, food security, and the preservation of indigenous knowledge, while highlighting the deep connections between human communities and their agricultural environments.

Exploring Hybridization as a Driver of Rapid Adaptation in Poke Milkweed (Asclepias exaltata)

This study aims to uncover the mechanisms behind rapid plant adaptation to novel environments. We are investigating whether hybridization between poke milkweed (Asclepias exaltata), an understory plant thriving in moist, shaded forests, and common milkweed (A. syriaca), which prefers sunny, well-drained fields, has enabled poke milkweed to adapt quickly to new habitats. The discovery of a stable population of poke milkweed in sunny field environments within the Virginia mountains presents a unique opportunity to study how hybridization may facilitate adaptation to novel conditions.