I am a planetary scientist and space physicist at the University of British Columbia’s Data Science Institute and Earth, Ocean and Atmospheric Sciences department (DSI, EOAS). I am passionate about enabling machine learning for scientific discovery. In January 2025 I will be joining the University of Alberta’s Physics and Electrical and Computer Engineering departments as an incoming faculty member.

My research focuses on understanding current planetary space environments, and their previous conditions, by studying newly available large datasets from planetary missions. This includes for Mars where I am a science team member on NASA’s MAVEN mission.

I was previously at the University of California Berkeley’s Space Sciences Lab (SSL) supported by the MAVEN mission and a new NASA program in interdiscplinary AI applications. My PhD thesis focused on developing interpretable methods for machine learning in planetary science with applications to plasma transport around Saturn and was supported by an NSF Graduate Research Fellowship and a NASA Earth and Space Sciences Fellowship.

I am interested in supporting the use of machine learning 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.