Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data
Fine-grain morphological classifications at astronomical scale.
I work on the research, development, and application of machine learning and optimization techniques to challenging scientific problems.
Working at the intersection of computer science, statistics, and domain science, I work other researchers to develop new methods and tools to solve complex problems. I work at the Institute for Data Intensive Engineering and Science at the Johns Hopkins University. I am also a member of the JWST Advanced Deep Extragalactic Survey (JADES) collaboration working to unlock the secrets of the early universe using the James Webb Space Telescope.
Research Projects
FitsMap
A tool for displaying astronomical images and their associated catalogs.
Galaxy-Halo Connection
Using interpretable machine learning to understand the connections between galaxy stellar mass, star formation rate, and dark matter halo mass.
Morpheus Core
The core framework code to scale up per-pixel machine learning methods to large astronomical images.
Partial-Attribution Instance Segmentation for Astronomical Source Detection and Deblending
A new technique for the detection and deblending of astronomical sources using deep learning.