Los Alamos National Laboratory

Los Alamos National Laboratory

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Amanda Ziemann

Amanda Ziemann

Phone (505) 667-9027


  • Biosciences
  • Strategies for disease surveillance and management
  • Epidemiology modeling
  • Computational Physics and Applied Mathematics
  • Algorithms
  • Applied Math
  • Earth and Space Sciences
  • Remote sensing
  • Nuclear Engineering and Technology
  • Nonproliferation
  • Science of Signatures - Remote and Standoff Sensing
  • Ground-based sensing and sensors
  • Space-based sensing and sensors
  • Multi-spectral
  • Hyperspectral for infrared, VIS, and UV
  • Integration of sensors
  • Remote sensing
  • Image analysis
  • Persistent surveillance
  • Real-time knowledge extraction
  • Hyperspectral data processing algorithms and analysis tools
  • Space-based EMP detection
  • Information Science and Technology
  • Social data analysis



Ph.D., Imaging Science, Concentration: Remote Sensing, Rochester Institute of Technology (2015)

M.S., Applied & Computational Mathematics, Rochester Institute of Technology (2011)

B.S., Applied Mathematics, Rochester Institute of Technology (2010)


LANL Positions

Scientist 2 (2017 - present)

Agnew National Security Postdoctoral Fellow (2016 - 2017)

Postdoctoral Research Associate (2015 - 2016)


Professional Societies




Book Contributions

A. Ziemann and S. Matteoli, “Detection of Large-Scale and Anomalous Changes," in Hyperspectral Image Analysis - Advances in Machine Learning and Signal Processing (S. Prasad and J. Chanussot, eds.), Springer, 2019  [to appear]

Selected Publications

J. Theiler, A. Ziemann, S. Matteoli, and M. Diani, “Spectral variability of remotely sensed target materials: Causes, models, and strategies for mitigation and robust exploitation,” IEEE Geoscience and Remote Sensing Magazine, vol. 7(2), June 2019

A. W. Bartlow, C. Manore, C. Xu, K. A. Kaufeld, S. Del Valle, A. Ziemann, G. Fairchild, and J. M. Fair, “Forecasting zoonotic infectious disease response to climate change: Mosquito vectors and a changing environment,” Veterinary Sciences, vol. 6(2), June 2019

A. Ziemann, C. X. Ren, and J. Theiler, “Multi-sensor anomalous change detection at scale,” in Proc. SPIE Defense + Commercial Sensing, vol. 10986(1098615), April 2019

A. Ziemann, M. Kucer, and J. Theiler, “A machine learning approach to hyperspectral detection of solid targets,” in Proc. SPIE Defense + Commercial Sensing, vol. 10644(1064404), April 2018

A. Ziemann and J. Theiler, “Simplex ACE: a constrained subspace detector,” Optical Engineering, vol. 56(8), June 2017

A. Ziemann, “Local spectral unmixing for target detection,” in Proc. Southwest Symposium on Image Analysis and Interpretation (SSIAI), IEEE, March 2016

J. Theiler and A. Ziemann, “Right spectrum in the wrong place: a framework for local hyperspectral anomaly detection,” in Proc. Int. Symposium on Electronic Imaging, February 2016

A. Ziemann and D. W. Messinger, “An adaptive locally linear embedding manifold learning approach for hyperspectral target detection,” in Proc. SPIE Defense, Security, and Sensing, vol. 9472 (94720O), April 2015

A. Ziemann, D. W. Messinger, and P. S. Wenger, “An adaptive k-nearest neighbor graph building technique with applications to hyperspectral imagery,” in Proc. Western New York Image and Signal Processing Workshop, IEEE, November 2014

D. W. Messinger, A. Ziemann, A. Schlamm, and W. Basener, “Metrics of spectral image complexity with application to large area search,” Optical Engineering, vol. 51(3), March 2012

K. Canham, A. Schlamm, A. Ziemann, B. Basener, and D. W. Messinger, “Spatially adaptive hyperspectral unmixing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49(11), October 2011

A. Ziemann, D. W. Messinger, and W. F. Basener, “Iterative convex hull volume estimation in hyperspectral imagery for change detection,” in Proc. SPIE Defense, Security, and Sensing, vol. 7695(76951I), May 2010