Abigail Azari

Area of Study / Keywords

Machine Learning Bayesian Statistics Space Physics Planetary Science Planetary Magnetospheres Space Environments Space Plasmas Photonics and Plasmas

About

I am an Assistant Professor jointly appointed in the departments of Physics and Electrical and Computer Engineering at the University of Alberta, a Canada CIFAR AI Chair, and a research Fellow at the Alberta Machine Intelligence Institute.

See my research website for more information (including student opportunities) and Google Scholar for my publications. 

Education

PhD, University of Michigan, College of Engineering, Atmospheric, Oceanic and Space Science, 2020
Funded through a NSF Graduate Research Fellowship, a NASA Earth and Space Science Fellowship, and a Rackham Merit Fellowship.

MS, University of Michigan, College of Engineering, Atmospheric, Oceanic and Space Science, 2017

BA, Smith College, Physics, 2013

Selected Professional Experience

Assistant Professor, University of Alberta, Dept. of Physics, Dept. of Electrical and Computer Engineering, Current

Canada CIFAR AI Chair & Research Fellow, Alberta Machine Intelligence Institute, Current

Post-Doctoral Researcher & Data Science Fellow, University of British Columbia, 2023 - 2025

Post-Doctoral Researcher, UC Berkeley's Space Sciences Lab, 2020 - 2023

Science Policy Fellow, IDA Science and Technology Policy Institute, 2013 - 2015

I also held various undergraduate research positions prior to 2013 at Princeton Plasma Physics Laboratory/DIII-D National Fusion Facility, NASA Jet Propulsion Laboratory, and Colorado School of Mines. 

ASTRO 429 - Upper Atmosphere and Space Physics

Basic space plasma phenomena: the Earth's plasma and field environment; the solar cycle; generation of the solar wind; the interplanetary plasma and field environment; the solar-terrestrial interaction; magnetospheric substorms; the aurora borealis; magnetosphere-ionosphere interactions; effects of magnetospheric storms on man-made systems; use of natural electromagnetic fields for geophysical exploration. Pre- or corequisite: PHYS 381.

ECE 342 - Probability for Electrical and Computer Engineers

Deterministic and probabilistic models. Basics of probability theory: random experiments, axioms of probability, conditional probability and independence. Discrete and continuous random variables: cumulative distribution and probability density functions, functions of a random variable, expected values, transform methods. Pairs of random variables: independence, joint cdf and pdf, conditional probability and expectation, functions of a pair of random variables, jointly Gaussian random variables. Sums of random variables: the central limit theorem; basic types of random processes, wide sense stationary processes, autocorrelation and crosscorrelation, power spectrum, white noise. Prerequisite: MATH 209. Credit may be obtained in only one of ECE 342 or E E 387.

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