Welcome to my website! I am a Ph.D. Candidate in Astronomy at the University of Florida.
With my advisor Prof. Sarah Ballard,
I study exoplanets around the smallest and coolest stars.
I recieved my Bachelor's degree in Astronomy & Physics
from Boston University in 2020. In the past, I have been a
Flatiron Pre-Doctoral Fellow in the
Center for Computational Astrophysics
at the Flatiron Institute,
an intern at the
Kepler/K2 Guest Observer office
at NASA Ames Research Center and the
FastML group
at CERN.
Click here to download my CV.
Stellar age measurements are fundamental to understanding a wide range of astronomical processes,
including Galactic dynamics, stellar evolution, and planetary system formation.
However, extracting age information from main-sequence stars is complicated, with techniques
often relying on age proxies in the absence of direct measurements.
The Gaia data releases have enabled detailed studies of the dynamical properties of stars within the Milky Way,
offering new opportunities to understand the relationship between stellar age and dynamics.
In this study, we leverage high-precision astrometric data from Gaia DR3 to construct a stellar age prediction
model based only on stellar dynamical properties, namely the vertical action.
We calibrate two distinct, hierarchical stellar age–vertical action relations,
first employing asteroseismic ages for red-giant-branch stars, then isochrone ages
for main-sequence turn-off stars. We describe a framework called zoomies based on this calibration,
by which we can infer ages for any star given its vertical action.
This tool is open-source and intended for community use. We compare dynamical age
estimates from zoomies with age measurements from open clusters and asteroseismology.
We use zoomies to generate and compare dynamical age estimates for stars from the Kepler,
K2, and TESS exoplanet transit surveys. While dynamical age relations are associated
with large uncertainty, they are generally mass independent and depend on
homogeneously measured astrometric data. These age predictions are uniquely useful
for large-scale demographic investigations, especially in disentangling the relationship
between planet occurrence, metallicity, and age for low-mass stars.
Leveraging the
photoeccentric effect
, we combine Kepler transit light curves
with stellar density information from spectroscopy and parallaxes from Gaia to
constrain the orbital eccentricities for over 150 planets around nearby M dwarfs.
Within a Bayesian hierarchical framework, we draw out the underlyling eccentricity
distribution for planets around M dwarfs and find two distinct populations:
single-transit, higher eccentricity planets and multi-transit, low-eccentricity planets.
We use planet occurrence information to constrain the volume-limited occurrence rate
of eccentric M dwarf planets. Read the paper in PNAS
As an undergraduate at Boston University, I worked with my advisors Prof. Philip Muirhead and Dr. Julie Skinner to constrain the
upper limit of planet occurrence around late-M and early-L dwarfs (or "ultracool dwarfs") in our local neighborhood. We conducted
a planet search around 827 ultracool dwarfs observed by NASA's K2 mission, and found none. Using this null result,
we constrained the upper limit of planet occurrence around ultracool dwarfs in the local neighborhood,
accounting for our transit detection efficiency and transit probability.
I published an open-source Python package called zoomies, which is an open-source tool to constrain stellar ages using only kinematic information
(vertical action from Gaia), a galactic potential model, and an external stellar calibration
sample. The package includes tools to calibrate a stellar age--vertical action relation and apply it to
your desired stellar sample. Since the age relation comes from purely kinematic information (galactic orbits),
the relation is nearly completely mass-independent, meaning you can use this tool to constrain ages for any stars
from supergiants to M dwarfs!
I also published an open-source Python package called photoeccentric,
which includes a suite of tools to process Kepler transit lightcurves,
calculate stellar densities, and perform transit fitting to constrain
eccentricities using only photometric data + stellar density priors.
I contribute to lightkurve,
a user-friendly, open-source Python package for working with Kepler, K2, and TESS data.
