Daniela Calvetti

James Wood Williamson Professor


2145 Adelbert Rd, Room 202

Other Information

Degree: PhD, University of North Carolina-Chapel Hill

Research interests:

  • Large Scale Scientific Computing
  • Computational Inverse Problems
  • Uncertainty Quantification

Predictive Mathematical Models for:

  • Neuroscience
  • Metabolism
  • Cellular Physiology


Selected recent publications:

  • D. Calvetti E. Somersalo and A. Strang (2019) Hierachical Bayesian models and sparsity: 2magic. Inverse problems 35 035003.
  • D. Calvetti, F. Pitolli, E. Somersalo and B. Vantaggi (2018) Bayes meets Krylov: Statistically inspired preconditioners for CGLS. SIAM Rev 60(2): pp.429-461.
  • D. Calvetti and E. Somersalo (2019) Brain energy metabolism. In: Jaeger D., Jung R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY.
  • D. Calvetti, A. Pascarella, F. Pitolli, E. Somersalo and B. Vantaggi (2018) Brain Activity Mapping from MEG Data via a Hierarchical Bayesian Algorithm with Automatic Depth Weighting. Brain Top. Aug 18, pp.1-31.
  • D. Calvetti and E. Somersalo (2018) Inverse problems: from regularization to Bayesian inference. WIREs Computational Statistics e1427

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