PNNL

Dr. Aaron Luttman is a Senior Technical Advisor at Pacific Northwest National Laboratory (PNNL), connecting AI capabilities across scientific domains to national security missions. A mathematician by training, Luttman began his career in industry as a research engineer in computer vision, then became a university professor before joining the Department of Energy complex. He spent nearly a decade at the Nevada National Security Site (NNSS) as a researcher, manager of people and programs, and technical advisor to the National Nuclear Security Administration (NNSA) Office of Experimental Sciences. In 2019, Luttman moved to PNNL, where his primary focus has been supporting the NNSA and the Department of Energy’s Genesis Mission, bringing PNNL’s AI capabilities to critical challenges in nuclear nonproliferation and defense programs.

Dr. Luttman has over 40 peer-reviewed publications, has given more than 150 invited and conference research presentations, and was a featured mathematician in the book 101 Careers in Mathematics, 4^th Ed. His work has been recognized with Clarkson University’s Outstanding New Teacher award, the NNSS President’s Award for Scientific Leadership, four NNSA Defense Programs Awards of Excellence, and a PNNL Core Values award. He is a nationally recognized expert on AI for national security, having served on the National Academies of Science, Engineering, and Medicine's workshop on AI and Justified Confidence, the MILA Institute's Harms and Risks of AI in the Military workshop, and Johns Hopkins University's WMD Risk Reduction Science and Policy Forum. Among his other activities, Luttman is still deeply committed to training the next generation of national security scientists, mathematicians, and engineers, teaching Data Science for National Security at Montana State University, serving on the external advisory board to the Department of Mathematics and Statistics at Washington State University, and representing business, industry, and government on mathematics education committees for the Mathematical Association of America and the Society for Industrial and Applied Mathematics (SIAM).

• Assured AI and machine learning
• Data science
• Inverse problems
• Imaging science

• Image processing
• Image segmentation
• Mathematical modeling
• Mathematical optimization
• Nuclear nonproliferation

• PhD in mathematics, University of Montana
• MS in mathematics, University of Minnesota Twin Cities
• BS in mathematics, Purdue University

• Applied Mathematics Education Committee, SIAM
• Special Interest Group on the Mathematics of Business, Industry, and Government, Mathematical Association of America
• SIAM Pacific Northwest Section Meeting Organizing Committee
• Industrial Advisory Board, Computational Mathematics Program, Embry-Riddle Aeronautical University

• PNNL Physical Detection and Deployment Systems Core Values Award for Creativity, 2025
• SIAM Visiting Lecturer, 2018–present
• Featured Mathematician in the book 101 Careers in Mathematics, 3rd Ed., 2019
• Mathematical Association of America Distinguished Lecturer, 2018
• President’s Distinguished Performance Award for Excellence in Scientific Leadership, NNSS, 2018
• Four Defense Programs Awards of Excellence for contributions to NNSA, 2018, 2016
• Outstanding New Teacher Award, Clarkson University, 2010

2021

• Adams J., M. Morzfeld, K. Joyce, M.M. Howard, and A.B. Luttman. 2021. "A Blocking Scheme for Dimension-Robust Gibbs Sampling in Large-Scale Image Deblurring." Inverse Problems in Science and Engineering 29, no. 12:1789-1810. PNNL-SA-150477. doi:10.1080/17415977.2021.1880398

2020

• Catenacci J., D.J. Constantino, B. Gall, and A.B. Luttman. 2020. "Tomographic Reconstruction of the Neutron Time-Energy Spectrum from a Dense Plasma Focus." IEEE Transactions on Plasma Science 48, no. 9:3135 - 3143. PNNL-SA-154647. doi:10.1109/TPS.2020.3012104
• Gehring A., J. Zier, P. Flores, T. Haines, K. Joyce, A.B. Luttman, and S. Richardson, et al. 2020. "Reduction of Radiographic Spot Size with Dual Diameter Sub-mm Rods." In 21st Biennial American Physical Society Conference on Shock Compression of Condensed Matter (SCCM2019), June 16-21, 2109, Portland, OR. AIP Conference Proceedings, edited by J.M.D. Lane, et al., 2272, Article No. 060013 College Park, Maryland: American Institute of Physics. PNNL-SA-153083. doi:10.1063/12.0000935

2019

• Gastleum, Z., B. Goldblum, T. Shead, C. Stewart, K. Miller, and A. Luttman, Integrating Physical and Informational Sensing to Support Nonproliferation Assessments of Nuclear-Related Facilities, Proceedings of the INMM Annual Meeting, 2019.

2018

• K. Joyce, Bardsley, J. M., and A. Luttman, Point Spread Function Estimation in X-ray Imaging with Partially Collapsed Gibbs Sampling, SIAM J. Sci. Comput., 40 (2018), no. 3, B766-B787.
• Udagedara, I. G., B. Helenbrook, A. Luttman, and J. Catenacci, Probabilistic Reduced Order Modeling using a Bayesian Approach, Amer. J. Math. Comput. Sci., 3 (2018), no. 2, 50-61.

2017

• Bennett, N., et al, and A. Luttman, Development of the Dense Plasma Focus for Short-Pulse Applications, Physics of Plasmas, 24, 012720 (2017).
• Bennett, N., et al, and A. Luttman, Kinetic Simulations of Gas Breakdown in Dense Plasma Focus, Physics of Plasmas, 24, 062705 (2017).

2016

• Blair, J., A. Luttman, and E. Machorro, A Generalized Peano Kernel Theorem for Distributions of Exponential Decay, J. Math. Anal. Appl., 433 (2016), 622-641.
• Fowler, M., M. Howard, A. Luttman, S. E. Mitchell, and T. J. Webb, A Stochastic Approach to Quantifying the Blur with Uncertainty for High-energy X-ray Imaging Systems, Inverse Probl. Sci. Engineering, 24 (2016), 353-371.
• Howard, M., M. Fowler, A. Luttman, S. Mitchell, and M. Hock∗, Bayesian Abel Inversion in Quantitative X-Ray Radiography, SIAM. J. Sci. Comput., 38 (2016), B396-B413. (This article was featured as the June 2016, SIAM Nugget Bytes article.)