Sandia National Laboratories
Brian M. Adams
Biography and CV
Contact Information


I am an applied mathematician working in the Optimization and Uncertainty Estimation department in the Computation, Computers, Information and Mathematics center. My academic background is in mathematical biology, but my research interests in computational mathematics are more broad, with strong ties to applications.

Research Interests

  • Inverse problems and sensitivity analysis with engineering and biological models using simulated and/or experimental data. Model calibration and extrapolation under uncertainty.
  • Algorithms combining optimization and uncertainty quantification. Non-intrusive methods for efficient uncertainty quantification.
  • Modeling and control theory, especially with biological applications, including population dynamics, in-host infection dynamics and optimal treatment interruption strategies.
  • Scientific computation, including simulation of models, optimization, and parallel computing.
  • Close collaboration with mathematicians, statisticians, engineers, and other disciplinary scientists to model systems and analyze data.

Upcoming and Ongoing Appearances

Current Projects

  • DAKOTA is a freely available software framework for large-scale engineering optimization and uncertainty analysis. I serve on the DAKOTA development team, and developed and maintain its interface to Matlab and its scaling capabilities including logarithmic as well as automatic and user-prescribed characteristic value scaling. I support DAKOTA users with applications and perform installation/debugging on various platforms.

  • I am developing a network-based model for disease propagation. The model will be used in an inverse problem context to determine the initial location and severity of a disease outbreak, given knowledge of early symptomatic people in hospitals. The model is high fidelity in that it accounts for all individuals moving about a geographic region. Our goals for it include the ability to simulate various diseases via modules, parallel scalability, reduced order modeling through population sampling, and flexibility in input network descriptions.

  • I am researching new algorithms to perform model calibration under uncertainty. The goal of this work is to use ensemble experimental data to characterize distributions of model input parameters in order to perform further model simulations and analysis.

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Brian M. Adams
Senior Member of Technical Staff
(505) 284-8845