This page features a small selection of recently published works. You can find a full list of works at Google Scholar.
Azari, A. R., Biersteker, J. B., Dewey, R. M., Doran, G., Forsberg, E. J., Harris, C. D. K., Kerner, H. R., Skinner, K. A., Smith, A. W., Amini, R., Cambioni, S., Da Poian, V., Garton, T. M., Himes, M. D., Millholland, S., and Ruhunusiri, S. [+31 co-signatories] “Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade”. Submitted to the NRC Planetary and Astrobiology Decadal Survey. Full text availiable via the decadal survey and arXiv.
Perspective on reccomendations to the planetary community to ensure the long-term success for integrating machine learning into planetary science.
Azari, A. R., Lockhart, J. W., Liemohn, M. W., and Jia, X. (2020). “Incorporating Physical Knowledge into Machine Learning for Planetary Space Physics”. Frontiers in Astronomy and Space Sciences. Acessible at arXiv and Frontiers.
This work provides an example of interpretable machine learning for the field of planetary space physics along with guidelines for future applications of machine learning to space physics problems. Within the Machine Learning in Heliophysics collection.
Azari, A. R., Jia, X., Liemohn, M. W., Hospodarsky, G. B., Provan, G., Ye, S. ‐Y., et al. (2019). “Are Saturn’s Interchange Injections Organized by Rotational Longitude?” Journal of Geophysical Research: Space Physics, 124. doi:10.1029/2018JA026196.
This work investigated if mass transport events most similar to Rayleigh-Taylor instabilities in Saturn’s space environment were related to ionospheric or upper atmopsheric conditions by purusing a large-scale data occurrence analyses to tease apart multiple dependencies. Can be found online at JGR Space Physics.
Azari, A. R., Liemohn, M. W., Jia, X., Thomsen, M. F., Mitchell, D. G., Sergis, N., et al. (2018). “Interchange injections at Saturn: Statistical survey of energetic H+ sudden flux intensifications.” Journal of Geophysical Research: Space Physics, 123, 4692–4711. doi:10.1029/2018JA025391.
Featured article in July 2018 for novel interdisciplinary visualizations. The work within this paper developed a physics-based method most similar to logistic regression to rank and classify transient of energetic material around Saturn (interchange injections). This created the first standardized comparison to several statistical surveys undertaken to understand the transport of mass around Saturn. Can be found online at JGR Space Physics.
Dewey, R., Slavin, J., Raines, J., Azari, A. R., Sun, W. “MESSENGER observations of flow braking and flux pileup of dipolarizations in Mercury’s magnetotail: Evidence for current wedge formation”.
This work discusses dipolarizations at Mercury and presents a cross-planetary comparison with Earth’s substorm current wedge formation. Accepted for publiication, 8/21/2020.
Liemohn, M. W., Azari, A. R., Ganushkina, N. Y., and Rastatter, L. “The STONE curve: A ROC-derived model performance assessment tool”. Earth and Space Science. Accesible at Earth and Space Science. Preprint acessible at ESSOAr, and arXiv.
This work describes a new assessment tool for continuous data-model comparisons most similar to the ROC curve for classifcation analyses.
Azari, A. R. (2020). “A Data-Driven Understanding of Plasma Transport in Saturn’s Magnetic Environment”. PhD Thesis. University of Michigan.
Full text accesible at DeepBlue Thesis Database.