I am a post-doctoral scholar at University of California, Berkeley’s Space Sciences Laboratory (SSL). At SSL I work to analyze spacecraft data to find our what the historical remnants of Mars’ magnetic field might tell us about the loss of Mars’ atmosphere. 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 environments. I am passionate about developing statistical techniques for enabling scientific discovery in space and planetary sciences, especially for supervised classification tasks. Previously, to my graduate education at the University of Michigan 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.
- 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
- characterizing Mars’ historical magnetic field to understand its previous atmospheric loss
- Interpretable Machine Learning Methods for Planetary Science
- Saturn’s Magnetospheric Transport
- The Evolution of Mars’ Magnetic Field
Interpretable Machine Learning Methods for Planetary Science
A surge of data-rich planetary missions have transformed our ability to understand large and complicated plaentary systems. Automated methods are advantageous in analyzing these data and managing spacecraft to outer planetary bodies and are quickly becoming necessary to handle the large-data sizes being returned. A major use of automation in the field, is for scientific analyses, in which our understanding of the automation process itself is of critical importance. In addition, planetary science data themselves pose challenges to off the shelf applications of machine learning. I’m interested in developing interpretable methods to address data challenges through integration of physical understanding into machine learning pipelines.
Saturn’s Magnetospheric Transport
In 2004 the Cassini spacecraft arrived at Saturn. For the next 13 years the mission collected the largest ever, amount of data on Saturn’s magnetic and space environment (magnetosphere). Due to Cassini, Saturn is now the second most observed magnetosphere after that of Earth and many of our previous expectations about the Saturn environment have been overturned from this data deluge. These unique datasets allow for the first large-scale statistical analyses of how mass moves around Saturn. It is now understood that a Rayleigh-Taylor (RT) like instabilities, called interchange, are instrumental in these processes by exchanging different plasma populations around Saturn. Saturn, and other giant magnetospheres, provide some of the best observations of these instabilities in a naturally evolving system allowing important studies that help fill in our understanding of transport around planetary bodies.
The Evolution of Mars’ Magnetic Field
The Mars of several billion years ago is a very differnt Mars than that of today. Several billion years ago, Mars was geologically active from core to crust, possessed a much thicker atmosphere, and had an internally produced magnetic field. Today, the remnants of this former field remain, in effect frozen into the surface of Mars. A currently orbiting spacecraft, MAVEN, has obtained measurements above these fields; allowing for the large-scale characterization of these remnants to provide insight into to the historical evolution and eventual, termination, of Mars’ formerly active field.