What is she best known for?
The fields of study she is best known for:
- Mathematical analysis
- Algebra
- Artificial intelligence
Her primary scientific interests are in Mathematical analysis, Algorithm, Regularization, Applied mathematics and Artificial intelligence.
The concepts of her Mathematical analysis study are interwoven with issues in Shrinkage, Haar wavelet and Shearlet.
Specifically, her work in Algorithm is concerned with the study of Fast Fourier transform.
Her work deals with themes such as Computation, Divergence, Convex optimization, Numerical analysis and Image restoration, which intersect with Regularization.
Her research in Applied mathematics intersects with topics in Estimation theory, Noise reduction, Newton's method and Linear combination.
Her biological study spans a wide range of topics, including Computer vision and Pattern recognition.
Her most cited work include:
- Deblurring Poissonian images by split Bregman techniques (290 citations)
- Fast Fourier transforms for nonequispaced data: a tutorial (225 citations)
- On the Equivalence of Soft Wavelet Shrinkage, Total Variation Diffusion, Total Variation Regularization, and SIDEs (223 citations)
What are the main themes of her work throughout her whole career to date?
Gabriele Steidl spends much of her time researching Algorithm, Mathematical analysis, Applied mathematics, Artificial intelligence and Regularization.
She studies Algorithm, namely Fast Fourier transform.
The Mathematical analysis study combines topics in areas such as Shrinkage, Haar wavelet and Shearlet.
The study incorporates disciplines such as Positive-definite matrix, Numerical analysis and Noise reduction in addition to Applied mathematics.
Her Artificial intelligence study incorporates themes from Computer vision and Pattern recognition.
Her Regularization research includes elements of Convolution, Computation and Image restoration.
She most often published in these fields:
- Algorithm (29.07%)
- Mathematical analysis (19.77%)
- Applied mathematics (15.12%)
What were the highlights of her more recent work (between 2017-2021)?
- Algorithm (29.07%)
- Lipschitz continuity (4.26%)
- Pure mathematics (11.24%)
In recent papers she was focusing on the following fields of study:
Gabriele Steidl mainly focuses on Algorithm, Lipschitz continuity, Pure mathematics, Applied mathematics and Function.
Her Algorithm study frequently draws connections to other fields, such as Principal component analysis.
Her Lipschitz continuity research is multidisciplinary, incorporating elements of Stiefel manifold, Discrete mathematics, Vector-valued function and Combinatorics.
Her Pure mathematics research includes themes of Space, Embedding, Smoothness and Shearlet.
Her Applied mathematics research incorporates elements of Probability density function, Grassmannian and Expectation–maximization algorithm.
Her Function research is multidisciplinary, relying on both Representation and Group, Group representation.
Between 2017 and 2021, her most popular works were:
- Numerical Fourier Analysis (34 citations)
- Fast Fourier Transforms for Nonequispaced Data (19 citations)
- Priors with Coupled First and Second Order Differences for Manifold-Valued Image Processing (18 citations)
In her most recent research, the most cited papers focused on:
- Mathematical analysis
- Algebra
- Artificial intelligence
Her primary areas of study are Algorithm, Lipschitz continuity, Computation, Mathematical analysis and Geodesic.
She is interested in Regularization, which is a field of Algorithm.
She has included themes like Curse of dimensionality, Energy functional, Maxima and minima, Data point and Stiefel manifold in her Lipschitz continuity study.
Her Computation study combines topics in areas such as Estimation theory, Maximum likelihood, Estimator, Multivariate statistics and Efficient algorithm.
Her work on Fourier analysis as part of general Mathematical analysis study is frequently linked to Scale, bridging the gap between disciplines.
She combines subjects such as Inverse problem, Continuous modelling, Finite difference, Convex analysis and Iterative reconstruction with her study of Geodesic.
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