I’m interested in a wide range of research topics, a by-product of being easily distracted as a young child. I particularly enjoy working with large datasets using the latest data-driven and statistical techniques.
White dwarfs are the evolutionary end-stages of nearly all stars in the Universe, and exhibit fascinating properties due to their density and composition. There are numerous unsolved problems in white dwarf physics, partly because they have been very difficult to observe in large numbers until recently.
For the past two years I have worked with Professor Nadia Zakamska on characterizing white dwarf atmospheres and predicting their temperatures and surface gravities with spectroscopic measurements. The main challenge is that most current theoretical white dwarf atmospheric models are proprietary and kept under lock and key.
Along with graduate student Hsiang-Chih Hwang, we developed a Python package (wdtools) that uses various computational techniques to reverse-engineer white dwarf atmospheric models and enable astronomers to predict stellar parameters from spectra. We currently have a publication in preparation describing our methods.
The vast majority of the galaxies we observe are too faint or distant for us to characterize their individual stars. However, using world-class space telescopes like the Hubble Space Telescope, we can resolve the stellar populations of nearby galaxies and use their color-magnitude diagrams to study their initial mass distribution, composition, and more. It’s an open problem in astrophysics whether all galaxies form in a similar fashion to our own Milky Way. The next generation of telescopes like JWST and WFIRST will probe these galaxies deeper than ever before, perhaps finally answering this problem.
However, there is a need for speedy and accurate techniques to analyse resolved stellar populations to infer parameters that described their formation. I am currently working with Dr Mario Gennaro at the Space Telescope Science Institute to develop a computational framework to fit star formation histories and initial mass functions to resolved stellar populations. We are currently building an open-source, user-friendly package (starwave) to enable the broader astronomical community to perform this kind of analysis.
The next few decades will likely see a revolution in manned spaceflight, due to new launch platforms like SpaceX and renewed public interest in sending people to other worlds like the Moon and Mars. The effects of such long-duration spaceflight on human physiology are not yet well understood. Various factors like radiation, lack of gravity, and even loneliness are being actively researched these days.
I am particularly interested in the effect of spaceflight on the vestibular system or inner ear, which governs balance and orientation. I work with Professor Mark Shelhamer at the Johns Hopkins School of Medicine to investigate this question using controlled human trials of simulated spaceflight. Along with a large team of student collaborators, we are developing a tool to analyze physiological signals (hsltools) and are in the process of applying it to data we collected on 50 subjects in 2019.