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Mathematics

D-Index
39
Citations
6641
World Ranking
2196
National Ranking
926

Overview

Daniela Calvetti is affiliated with Case Western Reserve University in the United States. Their research primarily spans the fields of Engineering and Computer Science. More specifically, the subfields of study include Artificial Intelligence, Computational Mechanics, Cognitive Neuroscience, Modeling and Simulation, and Electrical and Electronic Engineering.

The main topics addressed in their work cover a diverse range of areas, notably Sparse and Compressive Sensing Techniques, COVID-19 epidemiological studies, Gaussian Processes and Bayesian Inference, Numerical methods in inverse problems, Electrical and Bioimpedance Tomography, SARS-CoV-2 and COVID-19 Research, and Blind Source Separation Techniques.

Recent publications by Daniela Calvetti include:

  • "Sparsity promoting hybrid solvers for hierarchical bayesian inverse problems", 2020, Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)
  • "Bayesian particle filter algorithm for learning epidemic dynamics", 2021, Inverse Problems
  • "Bayesian dynamical estimation of the parameters of an SE(A)IR COVID-19 spread model", 2020, arXiv (Cornell University)
  • "Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models", 2024, SIAM/ASA Journal on Uncertainty Quantification
  • "Metapopulation Network Models for Understanding, Predicting, and Managing the Coronavirus Disease COVID-19", 2020, Frontiers in Physics

Frequent co-authors in Daniela Calvetti's work are Erkki Somersalo, Monica Pragliola, Alexander Hoover, Johnie Rose, and Alberto Bocchinfuso.

Common publication venues for their work include arXiv (Cornell University), Inverse Problems, SSRN Electronic Journal, SIAM Journal on Scientific Computing, and Brain Topography.

Daniela Calvetti has also contributed to academic literature through book publications. They have authored two books published by the Society for Industrial and Applied Mathematics: "Mathematics of Data Science: A Computational Approach to Clustering and Classification" (2020) and "The Less Is More Linear Algebra of Vector Spaces and Matrices" (2022). Additionally, they authored "Bayesian Scientific Computing" (2023), published by Springer Nature.

Best Publications

  • Tikhonov regularization and the L-curve for large discrete ill-posed problems

    D. Calvetti;S. Morigi;L. Reichel;F. Sgallari

  • AN IMPLICITLY RESTARTED LANCZOS METHOD FOR LARGE SYMMETRIC EIGENVALUE PROBLEMS

    D. Calvetti;L. Reichel;D. C. Sorensen

  • Application of ADI Iterative Methods to the Restoration of Noisy Images

    D. Calvetti;L. Reichel

  • Adaptively Preconditioned GMRES Algorithms

    J. Baglama;D. Calvetti;G. H. Golub;L. Reichel

  • Introduction to Bayesian Scientific Computing: Ten Lectures on Subjective Computing

    Daniela Calvetti;Erkki Somersalo

  • TIKHONOV REGULARIZATION OF LARGE LINEAR PROBLEMS

    Daniela Calvetti;Lothar Reichel

  • Computation of Gauss-Kronrod of quadrature rules

    D. Calvetti;G. H. Golub;W. B. Gragg;L. Reichel

  • Estimation of the L-Curve via Lanczos Bidiagonalization

    D. Calvetti;G. H. Golub;L. Reichel

  • Hypermodels in the Bayesian imaging framework

    Daniela Calvetti;Erkki Somersalo

  • Inverse problems: From regularization to Bayesian inference

    D. Calvetti;E. Somersalo

  • On the regularizing properties of the GMRES method

    Daniela Calvetti;Bryan Lewis;Lothar Reichel

  • Noninvasive electrocardiographic imaging (ECGI): application of the generalized minimal residual (GMRes) method.

    Charulatha Ramanathan;Ping Jia;Raja Ghanem;Daniela Calvetti

  • Regularized autoregressive analysis of intravascular ultrasound backscatter: improvement in spatial accuracy of tissue maps

    A. Nair;D. Calvetti;D.G. Vince

  • GMRES-type methods for inconsistent systems

    D. Calvetti;B. Lewis;L. Reichel

  • GMRES, L-Curves, and Discrete Ill-Posed Problems

    D. Calvetti;B. Lewis;L. Reichel

  • Fast Leja points.

    J. Baglama;D. Calvetti;L. Reichel

  • IRBL: An Implicitly Restarted Block-Lanczos Method for Large-Scale Hermitian Eigenproblems

    J. Baglama;D. Calvetti;L. Reichel

  • L-Curve and Curvature Bounds for Tikhonov Regularization

    Daniela Calvetti;Lothar Reichel;A. Shuibi

  • Iterative methods for the computation of a few eigenvalues of a large symmetric matrix

    J. Baglama;D. Calvetti;L. Reichel

  • L-Curve Curvature Bounds via Lanczos Bidiagonalization

    Daniela Calvetti;Per Christian Hansen;Lothar Reichel

  • Introduction to Bayesian Scientific Computing: Ten Lectures on Subjective Computing (Surveys and Tutorials in the Applied Mathematical Sciences)

    Daniela Calvetti;Erkki Somersalo

Frequent Co-Authors

Erkki Somersalo
Erkki Somersalo Case Western Reserve University
Lothar Reichel
Lothar Reichel Kent State University
Gene H. Golub
Gene H. Golub Stanford University
Marco Viceconti
Marco Viceconti University of Bologna
Jari P. Kaipio
Jari P. Kaipio University of Auckland
Walter F. Boron
Walter F. Boron Case Western Reserve University
Danny C. Sorensen
Danny C. Sorensen Rice University
Yoram Rudy
Yoram Rudy Washington University in St. Louis
Olaf Blanke
Olaf Blanke École Polytechnique Fédérale de Lausanne
Eric J. Arts
Eric J. Arts University of Western Ontario

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