I am a PhD candidate within the University of Michigan’s Climate and Space department. I work at the intersection of applying data science to analyze large amounts of in-situ and remote sensing data to answer fundamental questions about planetary systems. I am passionate about applying statistical techniques for scientific discovery in space and planetary sciences targeting large data and supervised classification tasks. Previously, I worked in science policy where I provided policy analysis and technical support to federal agencies on a variety of topics including climate data, STEM education, and space policy.
In 2004 the Cassini spacecraft arrived at Saturn. For the next 13 years the mission collected large amounts of data, resulting in a highly sampled environment and magnetic environment or magnetosphere of Saturn. Due to Cassini, Saturn is now the second most observed magnetosphere after that of Earth. Since the Cassini mission, many of our previous expectations about the Saturn environment have been overturned, from the role the largest moon Titan plays in the system to the rotational rate of the planet itself.
My PhD thesis is on characterization and identification of the transport of energetic material in the environment around Saturn. These are called interchange injections and are similar to a Rayleigh - Taylor instability which are seen in fusion reactors and nebula. Researching these processes greatly improves knowledge of planetary magnetospheres and contributes to comparative studies of potentially habitable planets and space weather risks at Earth.
Check out publications for my most recent work. Earlier work may be found on my Google Scholar.
I am leading the following projects:
- an evaluation of using Jupyter notebooks for teaching to develop a growth mindset based course on geoscience visualization and statistics (see Teaching),
- understanding transport, loss, and energization of energetic ions around Saturn through large-scale statistics, &
- broader lessons for methods of supervised machine learning for space physics missions, particularly for planetary science.
I am collaborating on the following projects:
- analysis of solar wind composition using machine learning to investigate plasma dynamics using ion abundances. Project Lead: Yeimy Rivera
- development of automatic identification and analysis of dipolarizations at Mercury. Project Lead: Ryan Dewey
- analysis of mass transport and evolution of high energy particle events around Saturn. Project Lead: Prof. Michael Liemohn