What is she best known for?
The fields of study she is best known for:
- Algorithm
- Geometry
- Mathematical analysis
Aria Abubakar spends much of her time researching Algorithm, Multiplicative function, Inverse scattering problem, Inverse problem and Mathematical optimization.
Her Algorithm research incorporates themes from Norm and Solver.
The study incorporates disciplines such as Regularization, Minification, Regularization, Permittivity and Conjugate gradient method in addition to Multiplicative function.
Her biological study spans a wide range of topics, including Iterative method and Applied mathematics.
Aria Abubakar connects Inverse problem with A priori and a posteriori in her research.
Her Mathematical optimization research incorporates elements of Computation and Source inversion.
Her most cited work include:
- Imaging of biomedical data using a multiplicative regularized contrast source inversion method (288 citations)
- A General Framework for Constraint Minimization for the Inversion of Electromagnetic Measurements (269 citations)
- Contrast Source Inversion Method: State of Art (251 citations)
What are the main themes of her work throughout her whole career to date?
Aria Abubakar focuses on Algorithm, Inverse problem, Mathematical analysis, Multiplicative function and Mathematical optimization.
Her Algorithm research includes elements of Pixel and Iterative reconstruction.
She has included themes like Iterative method and Jacobian matrix and determinant in her Inverse problem study.
Her research integrates issues of Inverse and Conjugate gradient method in her study of Mathematical analysis.
Her Multiplicative function study integrates concerns from other disciplines, such as Regularization, Applied mathematics and Source inversion.
Her work on Inverse transform sampling expands to the thematically related Mathematical optimization.
She most often published in these fields:
- Algorithm (34.80%)
- Inverse problem (20.38%)
- Mathematical analysis (18.18%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial intelligence (7.84%)
- Algorithm (34.80%)
- Iterative reconstruction (7.21%)
In recent papers she was focusing on the following fields of study:
Aria Abubakar mostly deals with Artificial intelligence, Algorithm, Iterative reconstruction, Interpretation and Supervised descent method.
Her Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition.
Her study brings together the fields of Inverse problem and Algorithm.
Her Inverse problem research is multidisciplinary, incorporating perspectives in Perspective and Inversion.
Aria Abubakar usually deals with Iterative reconstruction and limits it to topics linked to Electrical impedance tomography and Regularization and Non-linear least squares.
In her research, Borehole is intimately related to Image, which falls under the overarching field of Interpretation.
Between 2018 and 2021, her most popular works were:
- Supervised Descent Learning Technique for 2-D Microwave Imaging (22 citations)
- Study on Joint Inversion Algorithm of Acoustic and Electromagnetic Data in Biomedical Imaging (16 citations)
- Application of supervised descent method to transient electromagnetic data inversion (13 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Geometry
- Algorithm
Aria Abubakar mostly deals with Artificial intelligence, Interpretation, Algorithm, Iterative reconstruction and Convolutional neural network.
Her research in Artificial intelligence intersects with topics in Machine learning, Tomography and Pattern recognition.
Aria Abubakar interconnects Image, Computer vision, Deep learning and Borehole in the investigation of issues within Interpretation.
Her Algorithm study combines topics in areas such as Image resolution, Norm and Polygon mesh.
Her studies in Iterative reconstruction integrate themes in fields like Inverse transform sampling, Compressibility, Residual and Conjugate gradient method.
Her Electrical impedance tomography research incorporates themes from Multiplicative function, Regularization, Divergence and Discrete exterior calculus.
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