Maggie Johnson

Biographical Sketch

Maggie Johnson received her PhD in Statistics from Iowa State University in 2017. She also has a MS in Statistics from Iowa State University and a BS in Mathematics from the University of Minnesota, Twin Cities. Her broad research interests are in developing statistical methods for applications in the environmental and climate sciences. Some of her particular statistical research interests are in spatial and spatiotemporal statistics for large data, Bayesian hierarchical models, state-space modeling and uncertainty quantification.

Prior to joining JPL, Maggie was a postdoc in the 2017-2018 SAMSI Program on Mathematical and Statistical Methods for Climate and the Earth System where she had the opportunity to work in a variety of areas including: data fusion of remote sensing surface vegetation data, fine-scale spatiotemporal forecasting of air pollution, detection and attribution of climate change using climate models, and on developing a "Theory of Data Analysis Systems" (ToDAS). Maggie became a JPL postdoc in 2018 to continue her work on ToDAS with Amy Braverman. This work focused on setting forth a mathematical model for understanding trade-offs between costs (computation, data movement, etc.) and inferential uncertainties for spatial data analyses in decentralized distributed data systems.

Currently, Maggie is working on uncertainty quantification for retrievals by the Microwave Limb Sounder (MLS).


  • Microwave Limb Sounder (MLS)
  • ECOSTRESS Evapotranspiration Analysis

Selected Publications

Cawse-Nicholson, K., Braverman, A., Kang, Emily L., Li, M., Johnson, M., Halverson, G., Anderson, M., Haind, C., Gunson, M., and and Hook, S. (2020). “Sensitivity and uncertainty quantification for the ECOSTRESS evapotranspiration algorithm – DisALEXI,” International Journal on Applied Earth Observations and Geoinformation. Download.

Johnson, M., Caragea, P.C., Meiring, W., Jeganathan, C., Atkinson, P.M. (2019). “Bayesian Dynamic Linear Models for Estimation of Phenological Events from Remote Sensing Data,” JABES, 24(1). Download.

Guan, Y., Johnson, M., Katzfuss, M., Mannshardt-Hawk, E., Messier, K. P., Reich, B. J., Song, J. J. (2019). “Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles,” JASA Applications & Case Studies. To appear. Download.

Reehl, S., Stanfill, B., Johnson, M., Browning, N. B, Mehdi, B. L., Bramer, L. (2019). “Event detection for undersampled electron microscopy experiments: A control chart case study,” Quality Engineering. To appear.