# Dan Foreman-Mackey

I'm an Associate Research Scientist at the Flatiron Institute's Center for Computational Astrophysics. My main research interest is the application of probabilistic data analysis techniques to solve fundamental problems in astronomy. These days, I'm mostly using time domain surveys to discover new exoplanets, interpret the underlying population of these planets, and learn more about the variability of stars. I am also interested in the development of scientific software and open-source practices.

## Publications

My full list of publications is available on ADS but here are a few recent highlights:

## Talks

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, 2, 3, and 4) — 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.

## Code

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:
• emcee — Kick-ass MCMC sampling in Python. See the paper.
• celerite — Scalable 1D Gaussian processes. Implemented in C++ with Python bindings.
• George — Blazingly fast Gaussian processes for regression. Implemented in C++ with Python bindings.
• corner.py — Simple corner plots (or scatterplot matrices) in matplotlib.
• Daft — Pixel-perfect probabilistic graphical models using matplotlib. (Pretty much unmaintained at the moment but hopefully that will change some day).