I'm a graduate student at NYU working on next generation
astronomical data analysis under the supervision of David W. Hogg. When
I'm not writing code, I'm probably climbing rocks somewhere. Take a
look at my C.V. or scroll down
to read about some of the projects that I'm working on.
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
Below is a list of some recent interesting papers that I've worked on but
of my papers are on the ArXiv.
In this paper, we develop a framework
for hierarchical probabilistic inference of exoplanet
populations taking into account survey completeness,
detection efficiency, and observational uncertainties.
Applying our method to an existing catalog,
we find that Earth-like exoplanets are less common than previously thought.
This paper comes with
publicly released data
and open-source code.
My collaborators in applied math have developed
fast algorithms for solving dense linear systems.
In this paper, we use these algorithms to compute log-determinants and
apply these methods to model correlated noise in Kepler data.
In this paper, we present a popular open-source
Markov chain Monte Carlo package written in Python. There is also
online documentation and MIT-licensed
Most of my job involves writing scientific software. All of it lives on
my GitHub account and
some of the highlights are listed here:
Kick-ass MCMC sampling in Python.
See the paper.
Blazingly fast Gaussian processes for regression.
Implemented in C++ and Python bindings.
See the paper.
Simple corner plots (or scatterplot matrices) in matplotlib.
Python bindings to the
MAST Kepler API.
Pixel-perfect probabilistic graphical models using matplotlib.
Outside of work, I also make silly things on the internet.
Here are some of them: