What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mathematical analysis
- Eigenvalues and eigenvectors
Ting-Zhu Huang mainly investigates Algorithm, Artificial intelligence, Combinatorics, Mathematical optimization and Multi-agent system.
His Algorithm research is multidisciplinary, relying on both Hilbert space, Numerical analysis and Heaviside step function.
His research integrates issues of Rate of convergence, Computer vision and Pattern recognition in his study of Artificial intelligence.
Ting-Zhu Huang has included themes like Hadamard's inequality, Hadamard matrix, Pure mathematics, Upper and lower bounds and Eigenvalues and eigenvectors in his Combinatorics study.
His Mathematical optimization research includes themes of Image resolution, Total variation denoising and Image restoration.
His work investigates the relationship between Linear system and topics such as Complex system that intersect with problems in Applied mathematics.
His most cited work include:
- Deblurring and Sparse Unmixing for Hyperspectral Images (118 citations)
- Image restoration using total variation with overlapping group sparsity (96 citations)
- Tensor completion using total variation and low-rank matrix factorization (89 citations)
What are the main themes of his work throughout his whole career to date?
Ting-Zhu Huang spends much of his time researching Algorithm, Applied mathematics, Linear system, Matrix and Mathematical analysis.
Ting-Zhu Huang studies Regularization which is a part of Algorithm.
His study in Applied mathematics is interdisciplinary in nature, drawing from both Iterative method, Krylov subspace, Mathematical optimization, System of linear equations and Discretization.
The various areas that Ting-Zhu Huang examines in his Iterative method study include Numerical analysis, Toeplitz matrix and Coefficient matrix.
His Linear system study integrates concerns from other disciplines, such as Rate of convergence and Residual.
His research investigates the link between Matrix and topics such as Combinatorics that cross with problems in Eigenvalues and eigenvectors, Upper and lower bounds, Diagonally dominant matrix and Spectral radius.
He most often published in these fields:
- Algorithm (26.54%)
- Applied mathematics (26.11%)
- Linear system (20.38%)
What were the highlights of his more recent work (between 2017-2021)?
- Algorithm (26.54%)
- Applied mathematics (26.11%)
- Rank (6.58%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Algorithm, Applied mathematics, Rank, Regularization and Artificial intelligence.
His Algorithm research integrates issues from Tucker decomposition, Hyperspectral imaging, Matrix decomposition, Norm and Image restoration.
He has researched Applied mathematics in several fields, including Krylov subspace, Residual, Generalized minimal residual method, Discretization and Discontinuous Galerkin method.
His study looks at the intersection of Generalized minimal residual method and topics like Eigenvalues and eigenvectors with Linear system, Saddle point and Iterative method.
His study in the field of Total variation denoising also crosses realms of Ultrasound.
His Artificial intelligence research is multidisciplinary, incorporating elements of Computer vision and Pattern recognition.
Between 2017 and 2021, his most popular works were:
- FastDeRain: A Novel Video Rain Streak Removal Method Using Directional Gradient Priors (63 citations)
- On Leader–Follower Consensus With Switching Topologies: An Analysis Inspired by Pigeon Hierarchies (48 citations)
- Cauchy Noise Removal by Nonconvex ADMM with Convergence Guarantees (46 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mathematical analysis
- Algorithm
His primary areas of study are Algorithm, Rank, Regularization, Artificial intelligence and Matrix decomposition.
His biological study spans a wide range of topics, including Hyperspectral imaging, Streak, Tensor, Norm and Image restoration.
Ting-Zhu Huang works mostly in the field of Rank, limiting it down to topics relating to Tensor and, in certain cases, Applied mathematics and Minification, as a part of the same area of interest.
His work on Total variation denoising as part of his general Regularization study is frequently connected to High order, thereby bridging the divide between different branches of science.
The concepts of his Artificial intelligence study are interwoven with issues in Computer vision and Pattern recognition.
The various areas that Ting-Zhu Huang examines in his Matrix decomposition study include Tucker decomposition and Tensor completion.
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