Abby Azari

Abby Azari

Assistant Professor at University of Alberta, Fellow at amii

I am a planetary scientist and space physicist pursuing probabilistic machine learning and inverse methods for scientific insight. I lead a research group in the Faculties of Science and Engineering at the Unversity of Alberta and the Alberta Machine Intelligence Institute. I am generally interested in advancing uncertainty quantification and interpretable machine learning for large-scale data, or automated methods, in the Earth and space sciences.

The majority of my research focuses on understanding current planetary space environments, and their previous conditions, by utilizing machine learning on large datasets from planetary missions (e.g. Mars, Saturn, Mercury, the solar wind). I am a member of the Science Team of NASA’s MAVEN mission where I lead machine learning efforts for understanding Mars’ space environment.

Before joining the University of Alberta, I was at the University of British Columbia’s Data Science Institute and Earth, Ocean and Atmospheric Sciences department as a Data Science Fellow. I have previously worked at the University of California Berkeley’s Space Sciences Laboratory, IDA’s Science and Technology Institute, NASA’s Jet Propulsion Laboratory, the General Atomics DIII-D National Fusion Facility via the Princeton Plasma Physics Laboratory, and the Colorado School of Mines.

My PhD was from the University of Michigan College of Engineering’s Climate and Space department where I was a NSF Graduate Research Fellow and NASA Earth and Space Sciences Fellow. I have an undergraduate degree from Smith College in Physics.

Joining the Research Group

I am recruiting for Master’s and PhD students for Fall 2025 in Physics and ECE. Potential projects include: predicting space weather at Mars, probability distributions of the solar wind, and planetary aurorae.

If you are interested in joining the group please email me with: 1) a brief description of your research interests and how you see them fitting into the research group, 2) a brief description of your background (e.g. a CV or Resumé), and 3) what program(s) you are interested in.

Ideal candidates will have experience in data science (e.g. machine learning, statistics, Bayesian methods) and/or plasma science (e.g. fusion, astrophysics, space physics).

The research group is interdiscplinary and I support students who wish to join the group with other experience, and those who have taken a break in their professional journey. I am always interested in supporting motivated students and postdocs who are able to pursue independent funding.

Post Doctoral and Visitor Funding Opportunities

The following are several (always availiable) avenues to work with the research group. I encourage potential post docs and visitors to review these before contacting me.