What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Statistics
Jeffrey A. Fessler mainly investigates Iterative reconstruction, Algorithm, Artificial intelligence, Computer vision and Mathematical optimization.
Jeffrey A. Fessler has researched Iterative reconstruction in several fields, including Image resolution, Image quality, Image processing, Iterative method and Tomography.
Jeffrey A. Fessler combines subjects such as Function, Monotonic function and Statistical model with his study of Algorithm.
The various areas that Jeffrey A. Fessler examines in his Artificial intelligence study include Magnetic resonance imaging and Computed tomography.
His Computer vision research includes elements of Field, Positron emission tomography, Root mean square and Fourier transform.
His Mathematical optimization research incorporates themes from Nonlinear conjugate gradient method, Expectation–maximization algorithm, Jacobian matrix and determinant, Applied mathematics and Image restoration.
His most cited work include:
- Positron-emission tomography (1117 citations)
- Nonuniform fast Fourier transforms using min-max interpolation (909 citations)
- Space-alternating generalized expectation-maximization algorithm (861 citations)
What are the main themes of his work throughout his whole career to date?
Jeffrey A. Fessler spends much of his time researching Iterative reconstruction, Algorithm, Artificial intelligence, Computer vision and Mathematical optimization.
His work carried out in the field of Iterative reconstruction brings together such families of science as Image quality, Image resolution, Regularization, Iterative method and Tomography.
His Algorithm study incorporates themes from Image processing, Imaging phantom, Monotonic function and Rate of convergence.
The study incorporates disciplines such as Detector and Pattern recognition in addition to Artificial intelligence.
His study in Computer vision is interdisciplinary in nature, drawing from both Field, Fast Fourier transform and Medical imaging.
His Mathematical optimization research includes themes of Smoothing, Quadratic equation, Estimator, Applied mathematics and Image restoration.
He most often published in these fields:
- Iterative reconstruction (53.19%)
- Algorithm (48.49%)
- Artificial intelligence (35.40%)
What were the highlights of his more recent work (between 2018-2021)?
- Iterative reconstruction (53.19%)
- Algorithm (48.49%)
- Artificial intelligence (35.40%)
In recent papers he was focusing on the following fields of study:
Jeffrey A. Fessler mostly deals with Iterative reconstruction, Algorithm, Artificial intelligence, Pattern recognition and Imaging phantom.
His Iterative reconstruction study integrates concerns from other disciplines, such as Image quality, Image resolution, Inverse problem, Regularization and Computed tomography.
His Image quality study combines topics from a wide range of disciplines, such as Fast Fourier transform and Fourier transform.
His Algorithm research is multidisciplinary, incorporating perspectives in Image processing, Noise, Line search, Magnetic resonance imaging and Statistical model.
His work deals with themes such as Mean squared error, Noise, Projection and Spect imaging, which intersect with Imaging phantom.
The Noise study combines topics in areas such as Iterative method and Machine learning.
Between 2018 and 2021, his most popular works were:
- Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning (45 citations)
- Optimization Methods for Magnetic Resonance Image Reconstruction: Key Models and Optimization Algorithms (23 citations)
- Convolutional Analysis Operator Learning: Acceleration and Convergence (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Quantum mechanics
Iterative reconstruction, Artificial intelligence, Algorithm, Pattern recognition and Inverse problem are his primary areas of study.
His studies deal with areas such as Image quality, Cluster analysis, Magnetic resonance imaging, Convex optimization and Compressed sensing as well as Iterative reconstruction.
In his research on the topic of Image quality, Radiation dose is strongly related with Computed tomography.
His Artificial intelligence course of study focuses on Machine learning and Iterative method, Series, Filter and Training set.
His work on Gradient method as part of general Algorithm research is frequently linked to Kernel, bridging the gap between disciplines.
His research on Pattern recognition also deals with topics like
- Noise reduction that connect with fields like Mean squared error, Convolution, Pixel, Inpainting and Video processing,
- Imaging phantom together with Spect imaging, Monte Carlo method, Attenuation, Dosimetry and Noise.
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