Doctoral Research

University of Washington, Seattle

Advised by Prof. Nathan Kutz and Prof. Bing Brunton

January 2017-Present

The goal of my research is to develop and understand data-driven techniques for characterizing and modeling large-scale spatiotemporal dynamics. In recent years, there has been a rapid increase in the availability of high-dimensional, long-term measurements. Many measurements come from complex systems in which the governing equations are poorly understood or entirely unknown, which has motivated the development of modern data-driven techniques for analyzing spatiotemporal dynamics. These techniques must be computationally efficient and interpretable, thus providing insights into the underlying physics.

Research Intern

Facebook Reality Labs/Oculus Research

Advised by Dr. Qing Chao

Summer 2018, Summer 2019

Over two summers, I worked at Facebook Reality Labs as a research intern on the sensor team. There, I developed novel machine learning and computer vision based methods for characterizing data from state-of the-art depth sensor technology. Patent submitted to USPTO.

Undergraduate Research

Lawrence Berkeley National Laboratory

Advised by Dr. Dave Brown

January 2014 - May 2016

Mu2e is a planned particle physics experiment to be located at Fermilab with the goal of detecting the neutrinoless decay of a muon to an electron in the field of a nucleus. This process, if discovered, would be the first detection of Charge Lepton Flavor Violation. By analyzing the outputted waveforms from the tracker we can reconstruct the deposited energy along with a measurement of the particle’s momentum, deduce a particle’s identity. Using a prototype of a segment of the tracker, many characteristics of the waveforms were measured. With this information, we have developed algorithms to extract the energy as accurately as possible from the ADC waveform measurements. This analysis will be critical in obtaining the greatest amount of information about a particle species. These results were summarized in an undergraduate honor's thesis and submitted as internal documents to the Mu2e collaboration.