What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical analysis
- Artificial intelligence
His primary areas of study are Algorithm, Circulant matrix, Toeplitz matrix, Mathematical optimization and Mathematical analysis.
His research in Algorithm intersects with topics in Image, Wavelet, Computer vision and Impulse noise.
His Circulant matrix research includes themes of Matrix and Preconditioner.
The concepts of his Toeplitz matrix study are interwoven with issues in Jacobi method, Rate of convergence, Numerical analysis and Conjugate gradient method.
His Mathematical optimization research is multidisciplinary, incorporating elements of Point, Applied mathematics and Convex optimization.
His Mathematical analysis research is multidisciplinary, incorporating perspectives in Total variation denoising, Continuation method, Nonlinear system and White noise.
His most cited work include:
- Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization (836 citations)
- Conjugate Gradient Methods for Toeplitz Systems (660 citations)
- A Fast Algorithm for Deblurring Models with Neumann Boundary Conditions (363 citations)
What are the main themes of his work throughout his whole career to date?
Raymond H. Chan spends much of his time researching Algorithm, Conjugate gradient method, Circulant matrix, Applied mathematics and Toeplitz matrix.
His research integrates issues of Wavelet, Artificial intelligence, Image restoration and Impulse noise in his study of Algorithm.
In his study, Queueing theory is strongly linked to Iterative method, which falls under the umbrella field of Conjugate gradient method.
His studies in Circulant matrix integrate themes in fields like Pure mathematics, Condition number, Matrix, Preconditioner and Rate of convergence.
His work investigates the relationship between Applied mathematics and topics such as Mathematical optimization that intersect with problems in Numerical analysis and Newton's method.
His Toeplitz matrix research integrates issues from Positive-definite matrix, Hermitian matrix and Combinatorics.
He most often published in these fields:
- Algorithm (23.99%)
- Conjugate gradient method (16.61%)
- Circulant matrix (14.76%)
What were the highlights of his more recent work (between 2017-2021)?
- Algorithm (23.99%)
- Econometrics (5.54%)
- Artificial intelligence (14.02%)
In recent papers he was focusing on the following fields of study:
His main research concerns Algorithm, Econometrics, Artificial intelligence, Image and Image restoration.
His work carried out in the field of Algorithm brings together such families of science as Smoothing, Inverse problem, Tensor, Initialization and Point spread function.
His Artificial intelligence research includes elements of Measure, Computer vision and Pattern recognition.
His work on Color constancy is typically connected to Reflection as part of general Computer vision study, connecting several disciplines of science.
His Image restoration research is multidisciplinary, relying on both Regularization, Applied mathematics and Thresholding.
His Applied mathematics research incorporates themes from Cross-validation, Microscopy, Minimax problem, Total variation denoising and Kernel.
Between 2017 and 2021, his most popular works were:
- Mean–variance, mean–VaR, and mean–CVaR models for portfolio selection with background risk (16 citations)
- Discovery of microRNA-like RNAs during early fruiting body development in the model mushroom Coprinopsis cinerea. (10 citations)
- Linkage between piecewise constant Mumford-Shah model and ROF model and its virtue in image segmentation (10 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Artificial intelligence
Algorithm, Artificial intelligence, Image restoration, Econometrics and Image segmentation are his primary areas of study.
Raymond H. Chan is studying Computation, which is a component of Algorithm.
His research in Artificial intelligence focuses on subjects like Pattern recognition, which are connected to Spatial analysis and Pixel.
Raymond H. Chan combines subjects such as Thresholding, Linkage, Applied mathematics, Tikhonov regularization and Piecewise with his study of Image restoration.
Raymond H. Chan has researched Image segmentation in several fields, including Computational mathematics, Numerical analysis and Mathematical optimization.
His Segmentation study incorporates themes from Regularization and Regular polygon.
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