About

I am a Research Scientist at Caltech working within the data analysis group of the LIGO-Lab.

Gravitational-waves have given us a new window into extreme physical phenomena across our Universe. My research focuses on the application of novel computing paradigms to open questions in gravitational-wave astrophysics and cosmology. I am particularly interested in facilitating additional multi-messenger discoveries, using gravitational waves to probe fundamental physics, and exploring the use of machine learning techniques in data-driven astrophysics.

For a more complete description of my work, please see my CV.

Contact Me

Graphics

Please feel free to use images and animations from this page in talks with appropriate credit. I produced all content on this page. Unless explicitly noted, no real gravitational-wave data have been used. No proprietary data have been used in any situation.

The expected 90% confidence areas as a function of detected time before merger for an idealized, design AHKLV network acting at 100% duty cycle. Each line tracks one simulation. The right part shows a histogram of expected localizations at the time indicated by the vertical black line. (2021)
The matched filter signal-to-noise ratio for a 30 solar mass – 30 solar mass binary black hole. The top panel shows the last half second of the waveform (at an exaggerated amplitude) correlated with the data. The bottom panel shows the corresponding SNR as time progresses. (2020)
The matched filter signal-to-noise ratio for noise transient (glitch). The top panel shows the last half second of the waveform (at an exaggerated amplitude) correlated with the data. The bottom panel shows the corresponding SNR as time progresses. This demonstrates the shortcomings of using solely SNR to rank gravitational-wave candidates. (2020)
A demonstration of the properties of Gaussian noise. The amplitude distribution (histogram) of the time samples is very well modeled by a Gaussian. (2021)