Khachik Sargsyan

Khachik Sargsyan

Principal Member of Technical Staff

Sandia National Laboratories


I am a Principal Member of Technical Staff at Sandia National Laboratories in Livermore, California (currently remote-working from Brooklyn, New York).

My research evolves around uncertainty quantification (UQ), statistical learning and predictability analysis of physical and computational models. I have developed and applied methods for model reduction, UQ and data assimilation, targeting fundamental challenges such as structural errors, intrinsic stochasticity, high-dimensionality, limited data, discontinuities, and rare events, with a range of applications including climate modeling, chemical kinetics, turbulent combustion, fusion science, hardware architecture simulation.


  • Uncertainty quantification
  • Machine learning
  • Statistical modeling
  • Bayesian inference


  • Ph.D. in Applied and Interdisciplinary Mathematics, 2007

    University of Michigan, Ann Arbor

  • B.S. in Applied Mathematics and Applied Physics, 2002

    Moscow Institute of Physics and Technology

Post-Graduate Experience


Member of Technical Staff

Sandia National Laboratories, Livermore, CA

Apr 2010 – Present


  • Developing state-of-the-art algorithms for uncertainty quantification and statistical learning for computational models of physical phenomena,
  • Deploying software for various tasks in model development and data analysis pipeline, including uncertainty propagation, model calibration and statistical analysis.


  • Leading and participating in research proposals and scientific projects,
  • Hiring and mentorship of postdoctoral appointees and summer students.

Postdoctoral Appointee

Sandia National Laboratories, Livermore, CA

Jul 2007 – Apr 2010
Predictability analysis in stochastic dynamical systems.


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A novel modeling framework to secure efficiency and accuracy in real-time ensemble flood forecasting. Water Resources Research, 2020.


Embedded model error representation for Bayesian model calibration. International Journal for Uncertainty Quantification, 2019.


Compressive sensing adaptation for polynomial chaos expansions. 2019.


Streamflow, stomata, and soil pits: sources of inference for complex models with fast, robust uncertainty quantification. Advances in Water Resources, 2019.