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Dr. Amy Braverman

Senior Research Scientist

About

Bio

Dr. Amy Braverman is a statistician specializing in statistical methods for analysis and uncertainty quantification in remote sensing. After graduating from Swarthmore College in 1982 with a B.A. in Economics, Dr. Braverman worked for nearly a decade in litigation support consulting. She returned to graduate school at UCLA in the early 1990's where she earned an M.A. in Mathematics and Ph.D. in Statistics. In 1999 she began a post-doc at JPL, and has been with the Lab ever since. Dr. Braverman's early work was in the use of data compression methods for analysis of massive data sets. As her career advanced, she has worked in spatial and spatio-temporal statistics, data fusion, statistical methods for the evaluation of climate models, and most recently in Uncertainty Quantification. She has been at the forefront of JPL's efforts to bring rigorous UQ to the derivation of geophysical information from remote sensing observations collected by NASA and JPL instruments. Dr. Braverman finds special satisfaction in mentoring post-docs and young researchers to build capability in Statistics and UQ at JPL, and in collaborating with academic colleagues to connect their research to JPL and NASA problems.

Education

  • B.A. Economics (1982), Swarthmore College
  • M.A. Mathematics (1992), UCLA
  • Ph.D. Statistics (1999), UCLA

Research Interests

Statistical methodology and applications; uncertainty quantification for remote sensing; data science theory and practice; climate model diagnosis and evaluation; spatial and spatio-temporal statistical modeling and data fusion; analysis of massive geophysical data sets; analysis and modeling of complex systems.

Experience

Professional Experience

  • Senior Analyst, National Economic Research Associates (Los Angeles, CA) 1983-1987
  • Research Director, Micronomics Inc. (Los Angeles, CA) 1987-1991
  • Caltech Post-doctoral Scholar at the Jet Propulsion Laboratory (Pasadena, CA) 1999-2001
  • Scientist (2001-2004)/Statistician (2004-2012)/Principal Statistician (2012-2022)/Senior Research Scientist (2022-present), Jet Propulsion Laboratory (Pasadena, CA) 2001-present

Achievements

Awards & Recognitions

Publications

  1. Samuels, R., Carmon, N., Komoni, B., Hobbs, J., Braverman, A., Young, D., and Song, J.J. (2025). Estimation of Impact Ranges for Functional Valued Predictors, Journal of Agricultural, Biological, and Environmental Statistics, to appear.
  2. Darcy, M., Hamzi, B., Hobbs, Susiluoto, J., Braverman, A., and Owhadi, H. (2025). Learning dynamical systems from data: A simple cross-validation perspective, part II: Nonparametric kernel flows, Physica D: Nonlinear Phenomena, Volume 476, page 134641, doi: 10.1016/j.physd.2025.134641.
  3. Kutugoda, A., Kang, E., Kalmus, P., and Braverman, A. (2025). A Multivariate Spatial Statistical Model for Statistical Downscaling of Sea Surface Temperature in the Great Barrier Reef Region, Journal of the Royal Statistical Society, Series C., to appear.
  4. Lamminpaa, O.M., Susiluoto, J.I., Hobbs, J.M., McDuffie, J.L., Braverman, A., and Owhadi, H. (2024). Forward Model Emulator for Atmospheric Radiative Transfer Using Gaussian Processes and Cross Validation, Atmospheric Measurement Techniques, https://doi.org/10.5194/amt-2024-63.
  5. Leung, Kelvin, Thompson, David R., Susiluoto, Jouni, Jagalur-Mohan, Jayanth, Braverman, Amy, Marzouk, Youssef (2024). Evaluating the accuracy of Gaussian approximations in VSWIR imaging spectroscopy retrievals, IEEE Transactions on Geoscience and Remote Sensing, Volume 62, pages 1–12, doi: 10.1109/TGRS.2024.3411916.
  6. Thompson, Marten, Braverman, Amy, and Chatterjee, Snigdhansu (2022). A Dependent Multi-model Approach to Climate Prediction with Gaussian Processes, Environmental Data Science, doi:10.1017/eds.2022.24.
  7. Kalmus, P., Nguyen, H., Roman, J., Wang, T., Yue, Q., Hobbs, J., and Braverman, A. (2022). Data Fusion of AIRS and CrIMSS Near Surface Air Temperature, Earth and Space Science. doi: 10.1029/2022EA002282.
  8. Kang, E.L., Li, M., Cawse-Nicholson, K., and Braverman, A. (2022). Modeling Multivariate Spatial Processes for Large Data, Journal of the Indian Statistical Association, Volume 59, Number 2.
  9. Le, T., Natraj, V., Braverman, A., and Yung, Y. (2022). Evaluation of modeled hyperspectral infrared spectra against all-sky AIRS observations using different cloud overlap schemes, Earth and Space Science, Volume 9, Number 7, 10.1029/2022EA002245.
  10. Thompson, D.R., Braverman, A., Bohn, N., Broderick, P.G., Carmon, N., Eastwood, M., Fahlen, J., J., Green, R.O., Johnson, M.C., Roberts, D.A., and Susiluoto, J. (2021). Scene invariants for quantifying radiative transfer uncertainty, Remote Sensing of the Environment, Volume 260 doi: 10.1016/j.rse.2021.112432.
  11. Braverman, A., Hobbs, J., Teixeira, J., and Gunson, M. (2021). Post hoc Uncertainty Quantification for Remote Sensing Observing Systems, Journal on Uncertainty Quantification, Volume 9, Number 3, pages 1064–1093. doi: 10.1137/19M1304283.
  12. Ma, P., Kang, E.L., Braverman, A., and Nguyen, H. (2019). Spatial Statistical Downscaling for Constructing High-Resolution Nature Runs in Global Observing System Simulation Experiments, Technometrics, doi: 10.1080/00401706.2018.1524791.
  13. Marchetti, Y., Nguyen, H., Braverman, A., and Cressie, N. (2018). Spatial Data Compression via Adaptive Dispersion Clustering, Computational Statistics and Data Analysis, Volume 117, pages 138– 153. doi:10.1016/j.csda.2017.08.004.
  14. Braverman, A., Chatterjee, S., Heyman, M., and Cressie, N. (2017). Probabilistic Evaluation of Competing Climate Models, Applications of Statistics in Climatology, Meteorology, and Oceanography, Volume 3, pages 93–105. doi:10.5194/ascmo-3-93-2017.
  15. Hobbs, J., Braverman, A., Cressie, N., Granat, R., and Gunson, M. (2017). Simulation Based Uncertainty Quantification for Estimating Atmospheric CO2 from Satellite Data, Journal on Uncertainty Quantification, Volume 5, Number 1, pages 956–985. doi: 10.1137/16M1060765.
  16. Nguyen, H., Cressie, N., and Braverman, A. (2017). Multivariate Spatial Data Fusion for Very Large Remote Sensing Datasets, Remote Sensing, Volume 9, Number 2, DOI:10.3390/rs9020142.
  17. Nguyen, H., Katzfuss, M., Cressie, N., and Braverman, A. (2013). Spatio-Temporal Data Fusion for Very Large Remote Sensing Datasets, Technometrics, DOI: 10.1080/00401706.2013.831774.
  18. Nguyen, H., Cressie, N., and Braverman, A. (2012). Spatial Statistical Data Fusion for Remote- Sensing Applications, Journal of the American Statistical Association, 107, pp. 1004-1018.
  19. Braverman, A.J., Fetzer, E.J., Kahn, B.H., Manning, E.R., Oliphant, R.B., and Teixeira, J.A. (2012). Massive Data Set Analysis for NASA’s Atmospheric Infrared Sounder, Technometrics, Volume. 54, Number 1, doi: 10.1080/00401706.2012.650504.
  20. Braverman, A., Cressie, N., and Teixeira, J. (2011). A Likelihood-based Comparison of Temporal Models for Physical Processes, Statistical Analysis and Data Mining, Volume 4, Number 3, pp. 247- 258, doi: 10.1002/sam.10113.
  21. Braverman, A. and Kahn, B. (2004). Visual Data Mining for Quantized Spatial Data, in Proceedings in Computational Statistics 2004, Antoch, J. (ed.). Physica-Verlag/Springer, Prague, pp. 61-72.
  22. Braverman, A., Fetzer, E., Eldering, A., Nittel, S., and Leung, K. (2003). Semi-streaming Quantization for Remote Sensing Data, Journal of Computational and Graphical Statistics, Volume 12, Number 4, pp. 759-780, pp. 429–441.
  23. Braverman, A. (2002). A Strategy for Compression and Analysis of Massive Geophysical Data Sets, in Lecture Notes in Statistics: Nonlinear Estimation and Classification, Denison, D.D., (ed.). Springer- Verlag, New York.
  24. Braverman, A. (2002) Compressing Massive Data Sets Using Quantization, Journal of Computational and Graphical Statistics, Volume 11, Number 1, pp. 44-62.
  25. Braverman, A. and DiGirolamo, L. (2002). MISR Global Data Products: A New Approach, IEEE Transactions on Geoscience and Remote Sensing, Volume 40, Number 7, pp. 1626-1636.
  26. Kahn, R.A., and Braverman, A. (1998). What Shall We Do with the Data We Are Expecting from Upcoming Earth Observing Satellites? Journal of Computational and Graphical Statistics, Volume 8, Number 3, pp. 575-588.