My research is at the intersection of computer science and astronomy.
I mostly work on principled data analysis methods for astronomical
images, drawing on old and new results from computer vision, machine
learning, signal processing, information theory, and statistics. On
the astronomy side, the goal of my work is to make the best possible
use of the data we have to answer scientific questions and to discover
new questions to be asked. On the computer science side, I use
astronomical images, with their beauty and underlying simplicity, as a
motivator for the development of new machine learning and inference
models and methods.
Lately, I have been working on combining imaging datasets taken by
different astronomical instruments in order to benefit from their
complementary wavelength coverage, sensitivity, and resolution.
Specifically, I have been focusing on simultaneous analysis of imaging
data from
the Wide-Field
Infrared Survey Explorer (WISE) satellite and ground-based optical
imaging from the Sloan Digital Sky
Survey (SDSS). SDSS provides nearly ten times better resolution,
which allows us to interpret the WISE imaging by resolving nearby
stars and galaxies that are blended together in the WISE images. For
this work, we have developed a generative model for astronomical
images, dubbed the Tractor. As
part of my work with the WISE data, I had to produce a new set of
combined images (coadds, in the astronomical jargon) from the
multi-exposure WISE data; this project is
called unWISE.
My results are being used to select quasar and Luminous Red Galaxy
targets for spectroscopy in the SDSS-3/SEQUELS program, and the
upcoming SDSS-4/eBOSS program, which will map huge swaths of the
high-redshift universe to make measurements of the baryon acoustic
oscillation scale and nail down the growth history of the universe.
The generative image modelling work we are doing with the
Tractor will be used for imaging data analysis and targeting of
the Dark Energy Spectroscopic
Instrument (DESI) project; we are actively collaborating with
statisticians from Berkeley and Harvard to explore the large-scale
inference challenges presented.
I support and have research interests in amateur astronomy, through
the Astrometry.net project (in
particular the web
service nova.astrometry.net),
and through some projects exploring how we could use the data produced
by amateur astronomers for scientific purposes,
including reconstructing the
orbit of Comet Holmes using web image search and combining images
of unknown provenance and calibration into an open-source sky
survey.