I am a planetary scientist and space physicist at the University of California, Berkeley where I am a post-doctoral scholar in the Space Sciences Laboratory (SSL). I am passionate about developing statistical techniques to enable data-driven discovery. My work intersects space physics and planetary science, with techniques from geosciences, statistics, data visualization, and computer science.

Most of my work focuses on understanding current planetary environments, and their previous conditions, by studying newly available large datasets from planetary missions. My current research is focused on analyzing spacecraft data to understand current and past planetary conditions on Mars and is partially funded through a new program in AI Use Cases sponsored by NASA. My recent work, as part of my PhD thesis supported by an NSF Graduate Research Fellowship and a NASA Earth and Space Sciences Fellowship focused on developing interpretable methods for machine learning in planetary science with applications to plasma transport around Saturn.

I am interested in supporting the use of statistical and large-data methods for science and developing educational resources in these practices. These efforts have included co-creating the Machine Learning for Planetary Space Physics seminar series, leading a community generated white paper for incorporating machine learning in planetary science for the next decade of missions, and serving on the Planetary Data Ecosystem Independent Review Board Subcommittee on Mining and Automation. Additionally, I have co-developed a geoscience visualization and statistics course for physical science students new to computer programming which received an award from the University of Michigan for outstanding instruction.