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Jason Bernstein

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  • Title
    Technical Staff
  • Email
    bernstein8@llnl.gov
  • Phone
    (925) 422-9816
  • Organization
    ENG-CED-COMPUTATIONAL ENGINEERING

I am a member of the Applied Statistics Group at Lawrence Livermore National Laboratory (LLNL), where I develop statistical and data science methods for projects spanning stockpile stewardship, nonproliferation, space domain awareness, materials science, reliability, software testing, and operations data analytics. Sample projects include multiple space object tracking, satellite collision probability estimation, equation of state prediction, and Bayesian calibration of material strength models. I have also managed projects and collaborations with industry, academia, and government partners.

I am the Director of the Data Science Institute Consulting Service and the Open Data Initiative at LLNL. The consulting service helps Lab staff with topics such as artificial intelligence, data management, and surrogate modeling. The Open Data Initiative is a catalog of open LLNL datasets that support research and collaboration.

John Tukey said that "The best thing about being a statistician is that you get to play in everyone’s sandbox," and I have found this to be very true at LLNL.

Ph.D., Statistics, Pennsylvania State University, 2016

M.Sc., Statistics, Pennsylvania State University, 2014

B.Sc., Mathematics, Michigan State University, 2010

Bernstein, J., Myint, P. C., Lindquist, B. A., & Brown, J. L. (2026). A tutorial on Bayesian analysis of linear shock compression data. Journal of Applied Physics, 140(3), 031101.

Hallas, K. L., De Jesus, M., Wu, C. J., Li, J., Bernstein, J., & Myint, P. C. (2026). Toward Mapping Multiphase Multicomponent Mixtures with Neural Networks. Data Science in Science, 5(1), 2631836.
 
Foster, M., Steirer, K., Bernstein, J., Herynk, M., & Lamberson, L. (2025). Influence of pore geometry and distribution on buckling under micro computed tomography. Polymer, 128434.
 
Muyskens, A. L., Goumiri, I. R., Priest, B. W., Schneider, M. D., Armstrong, R. E., Bernstein, J., & Dana, R. (2022). Star–galaxy image separation with computationally efficient Gaussian process classification. The Astronomical Journal, 163(4), 148.
 
Rivera, D., Bernstein, J., Schmidt, K., Muyskens, A., Nelms, M., Barton, N., ... & Florando, J. (2022). Bayesian calibration of strength model parameters from Taylor impact data. Computational Materials Science, 210, 110999.
 
Bernstein, J. (2021). Probabilistic data association for orbital-element estimation using multistage expectation–maximization. Journal of Aerospace Information Systems, 18(5), 250-268.
 
Bernstein, J., Filippov, A., Schneider, M., & Miller, C. (2021). Quantifying Uncertainty in All-to-All Estimates of Space Object Conjunction Probabilities using U-Statistics (No. LLNL-TR-827439). Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States).
 
Schunck, N., Quinlan, K. R., & Bernstein, J. (2020). A Bayesian analysis of nuclear deformation properties with Skyrme energy functionals. Journal of Physics G: Nuclear and Particle Physics, 47(10), 104002.
 
Bernstein, J., & Fricks, J. (2016). Analysis of single particle diffusion with transient binding using particle filtering. Journal of theoretical biology, 401, 109-121.