Department of Biology

Lab

Smith Lab website

Research

I explore machine learning approaches in ecological genetics. Genetic information are valuable for characterizing a population's spatial connectivity, migration with neighboring populations, phylogeographic and demographic history, and adaptation to the local environment. However, the complexity of genetic variation makes it challenging to model. For this task machine learning is a fascinating avenue because of its ability to find useful information in high-dimensionality data, and because new techniques are emerging rapidly. My interests lie in adapting these new approaches to the unique properties of genomic data, innovating deep learning architectures, and developing computational toolsets for other researchers to implement in diverse species.

In addition, I am curious about the types of genomic changes that underlie evolutionary transitions, as well as microbial ecology related to population divergence or methane emissions. While I don't have a particular study organism, I have an affinity for wolves, sunflowers, apple trees, barn swallows, fruit flies, and sticklebacks.