I'm a PhD candidate in the Quantitative Genetics Lab at UC Davis, working under Dr. Hao Cheng.

I got my start in plant breeding when I was an undergraduate at North Carolina State University and had the chance to work at the International Potato Center under Dr. Gabriela Burgos in Lima, Peru for 3 months. I enjoyed it so much I started working for the Sweetpotato and Potato Breeding and Genetics Program at NCSU afterwards, under Dr. Craig Yencho and Ken Pecota. There, as a Master's student, I worked on developing better methods to measure traits in tablestock and ornamental sweetpotato, among other projects, and in the process discovered a lot of potential for "data science" (statistical models, visualization, image analysis, machine learning...) alongside conventional methods to solve problems in breeding.

I now work at the intersection of plant breeding and quantitative genetics using data science methods. Mainly, I'm developing methods to predict useful but hard-to-predict multidimensional phenotypes (images and text) from SNP markers using a combination of neural networks for latent space embedding and linear mixed models for prediction. In other words, predicting a picture or description of a plant from its genetic information. I collaborate with the Strawberry Breeding & Research Program and other breeding programs at UC Davis to make this new avenue for prediction a reality using their breeding data. I also work in other projects related to data in breeding, including a web app (https://mtwatson.shinyapps.io/G2P-datasets/) to access public genomic prediction datasets for education and benchmarking. Additionally, I'm a graduate student breeder in the Student Collaborative Organic Plant Breeding Education (SCOPE) Project, working to develop celtuce (莴笋) varieties for a longer harvest window in northern California.