Current Work: University of Washington & Pacific Northwest Seismic Network (Postdoctoral Scholar, 2025-)

I recently began as a postdoctoral scholar at the University of Washington under the supervision of Renate Hartog, in association with the Pacific Northwest Seismic Network (PNSN). I will be working on performance evaluation and development of an algorithm called GFAST, which is the component of the ShakeAlert earthquake early warning system which utilizes Global Navigation Satellite System (GNSS) data to determine the magnitudes of very large earthquakes.

University of Oregon (PhD, 2019-2024)

I pursued my PhD at the University of Oregon with Diego Melgar in his large earthquake science group. My dissertation focused on potential methods for improving how quickly and accurately we can characterize earthquakes as they occur, as well as immediately following them. I explored how we could use non-traditional data (a.k.a., data not from seismometers) used for Earth monitoring, as well as modern data processing and analysis techniques (such as machine learning), to improve the capabilities of the systems we rely on for earthquake disaster response.

One of my projects explored the question of how early earthquakes are distinguishable by magnitude using borehole strainmeters, where we found that earthquakes do not appear to be strongly deterministic (i.e., large earthquakes are not inherently different from small earthquakes in their beginning stages). This has implications for how long it takes to accurately determine the magnitude of an earthquake, particularly for large events which rupture over longer periods of time. A manuscript about this work is currently in preparation.

I also developed a machine learning algorithm aimed at improving the performance of earthquake early warning systems during large earthquakes. GNSS data is extremely useful for this purpose, as the displacements they record during earthquakes do not saturate at large magnitudes – but they are very noisy. My algorithm allows for discrimination between noisy GNSS waveforms which do actually contain seismic waves from earthquakes and those which do not, which can enable a reduction in the amount of high-noise/low quality data that enters algorithms which determine the magnitude of such earthquakes. I am also currently preparing a manuscript about this work.

2022 USGS NEIC Pathways Internship

During the summer of 2022, I worked with Will Yeck and Hank Cole at the National Earthquake Information Center (NEIC) in Golden, Colorado. We used a dataset called MLAAPDE (pronounced “millipede”) to train a global machine learning model for rapid earthquake magnitude characterization. MLAAPDE contains data from the NEIC’s Preliminary Determination of Epicenters catalog, and is specifically designed to facilitate research with machine learning methods. Our model, AIMag, allows for rapid estimation of single-station earthquake magnitudes using raw three-component P-waveforms observed at local to teleseismic distances, independent of prior size or location information. It was published in the Bulletin of the Seismological Society of America (BSSA) in January 2024.

2018 IRIS Internship

Over the summer of 2018, I worked at the USGS’s Albuquerque Seismological Laboratory in New Mexico as an IRIS intern with Adam Ringler on a project about the characteristics and spatial variability of wind noise recorded by broadband seismometers. (This program is now called the EarthScope RESESS internship.) The results of this research were presented as a poster at the 2018 AGU Fall Meeting, and were published in BSSA in March 2019. A link to my poster can be found on my Publications, Recorded Talks, & Posters page.

Washington University in St. Louis (B.A., 2015-2019)

As an undergraduate student, I worked with Jill Pasteris in her Raman spectroscopy lab analyzing a variety of minerals in a variety of settings, but primarily revolved around the mineral apatite. My main work was on an NSF project that investigated how to control lead corrosion in pipes that carry drinking water, which is what caused the health catastrophe in Flint, Michigan. Using a Raman microprobe, I analyzed scales on the insides of used lead pipes to try and identify the different mineral phases that had precipitated. Dr. Pasteris presented a poster including some of this work at the 2018 Goldschmidt Conference, which can also be found on my Publications, Recorded Talks, & Posters page.

2014 Discovery Corps Research Internship

As a high school student employed at the Pacific Science Center in Seattle, I had the opportunity over a summer to work with Julianna Simon at the University of Washington. I participated in a NASA project about using medical ultrasound to detect and treat kidney stones in astronauts, where I investigated what ultrasonic frequencies and stone surface types caused an artifact called “twinkling” to appear. At the end of the summer, I presented a poster and hands-on activity about sound waves at the Pacific Science Center to visitors, and the following year, Dr. Simon presented a poster including some of this work at the 2015 NASA Human Research Program Investigators’ Workshop. You can also find it on my Publications, Recorded Talks, & Posters page.