About me

Astronomer and public educator

I am an Assistant Professor in the Department of Physics and Astronomy at California State University, Long Beach. Previously, I held a NSF Astronomy and Astrophysics Postdoctoral Fellowship at the American Museum of Natural History. My research uses asteroseismology to deliver precise and accurate stellar parameters for Galactic archaeology. I'm also interested in the intersection of art and astronomy for public education.

Bio

I completed my Ph.D. under the supervision of Marc Pinsonneault at Ohio State University, where we worked at the intersection of Kepler, K2, and Gaia; I finished my PhD as a KITP Graduate Fellow. The focus of this doctoral work was to simultaneously characterize Gaia parallax systematics and test asteroseismic scaling relations. I went on to work as a postdoc at the University of New South Wales with Dennis Stello on K2 and TESS asteroseismology. I hold an A.B. magna cum laude from Princeton University in Astrophysical Sciences, where I worked with Michael Strauss on identifying AGN-galaxy strong lenses in SDSS; Richard Gott on predicting Slow-Roll dark energy constraints with Planck; and David Spergel on AGN-galaxy magnification weak lensing in WISE and BOSS.

Outreach

I have developed, in collaboration with the AMNH Education department, a creative, inquiry-based after school program for the American Museum of Natural History, where students from artistic backgrounds explore the universe through their craft.

Research projects

K2 Galactic Archaeology Program

K2 GAP is providing 19,000 asteroseismic masses and radii across Galactic environment; Data Release 2 provides an installment of 4,000 asteroseismic masses.

Automated asteroseismology

The Bayesian Asteroseismology data Modelling pipeline (GAP) identifies likely solar-like oscillators and returns asteroseismic values for them.

Accuracy of asteroseismology

By comparing Kepler asteroseismic radii to Gaia radii, we find asteroseismic scaling relations are accurate at the percent level.

Gaussian processes for LSST stars

Gaussian processes provide useful information for machine-learning stellar type classification beyond the typical features usually constructed from Fourier methods. Quasi-periodic Gaussian processes will be particularly useful for LSST stellar classification.