I’m a graduate student in the Seung Lab at Princeton University working on connectomics of samples imaged by electron microscopy (EM). I’m also generally interested in applications of machine learning to problems of broad impact, climate change, or social good.

Most of my graduate work has focused on synapse detection and assignment in EM volumes using convolutional networks. I built a software system for performing this task at scale, and our lab has also used the same system to perform automated segmentation of mitochondria and cell nuclei. I’ve also done some work on analyzing the connectivity patterns of cortical networks, and determining the cell types of ganglion cells in the mouse retina.

Before my graduate program, I did a research fellowship with Karen Berman at the NIMH using neuroimaging techniques. I analyzed diffusion-weighted imaging data to help characterize the white matter of children with Williams Syndrome, and analyzed gray matter volume distributions across typically developing volunteers.