I'm a Sagan Postdoctoral Fellow at the University of Washington working on
next generation astronomical data analysis.
My main research interest is the application of probabilistic data
analysis techniques to interesting datasets in astronomy. These days,
I'm mostly working with Kepler data; everything from the raw pixel
values to catalog level inferences. I'm also interested in the
development of scientific software and open-source practices.
My full list of publications is available
but here are a few recent highlights:
D. Foreman-Mackey, B. T. Montet, D. W. Hogg, T. D. Morton,
D. Wang, and B. Schölkopf,
search for transiting planets in the K2 data.
D. Foreman-Mackey, D. W. Hogg, and T. D. Morton,
population inference and the abundance of Earth analogs
from noisy, incomplete catalogs
D. Foreman-Mackey, D. W. Hogg, D. Lang, and J. Goodman,
emcee: The MCMC Hammer
I post most of my slides to Speaker Deck
but here are a few that I really love giving:
Exoplanet population inference
Slides for my talk about Foreman-Mackey,
Hogg, & Morton (2014).
An astronomer's introduction to Gaussian Processes
A motivation and introduction to the use of Gaussian Processes
in astronomy. The level is aimed at interested astronomers who
have experience fitting models to data.
Licenses in the wild
— I gave this talk at the 225th meeting of the American Astronomical
Society. It is about software licensing and it's based on my analysis
of 1.5 million repositories from GitHub.
Hack & Tell (1,
One of my favorite events in NYC is the Hack
& Tell Meetup where local coders, hackers, and makers talk for 5 minutes
each about fun projects they've been working on in their spare time. I've
presented at this meetup 4 times so far and it's always been a blast.
I write a lot of code for work and in my spare time. All my projects live in
public repositories on GitHub.
Here are some of my most popular research codes:
Kick-ass MCMC sampling in Python. See the paper.
Blazingly fast Gaussian processes for regression. Implemented in C++ with Python bindings.
Simple corner plots (or scatterplot matrices) in matplotlib.
Pixel-perfect probabilistic graphical models using matplotlib.
(Pretty much unmaintained at the moment but hopefully that will
change some day).