What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Mathematical analysis
His scientific interests lie mostly in Artificial intelligence, Brain mapping, Voxel, Pattern recognition and White matter.
His studies deal with areas such as Persistent homology, Gaussian function, Computer vision, Algorithm and Machine learning as well as Artificial intelligence.
His study looks at the relationship between Gaussian function and fields such as Geometry, as well as how they intersect with chemical problems.
His work carried out in the field of Brain mapping brings together such families of science as Frontal lobe and Orbitofrontal cortex.
In the subject of general Pattern recognition, his work in Kernel smoother is often linked to Barcode, thereby combining diverse domains of study.
His study in White matter is interdisciplinary in nature, drawing from both Cognitive skill, Diffusion MRI, Autism, Neuroscience and Early childhood.
His most cited work include:
- A unified statistical approach to deformation-based morphometry. (318 citations)
- Functional but not structural subgenual prefrontal cortex abnormalities in melancholia (308 citations)
- Early stress is associated with alterations in the orbitofrontal cortex: a tensor-based morphometry investigation of brain structure and behavioral risk. (292 citations)
What are the main themes of his work throughout his whole career to date?
Moo K. Chung spends much of his time researching Artificial intelligence, Pattern recognition, Smoothing, Algorithm and Persistent homology.
His study looks at the relationship between Artificial intelligence and topics such as Diffusion MRI, which overlap with White matter and Neuroimaging.
His Pattern recognition research integrates issues from Twin study, Resting state fMRI and Human brain.
His Smoothing research is multidisciplinary, incorporating perspectives in Gaussian function, Heat kernel, Mathematical analysis, Spherical harmonics and Surface.
His Resampling study, which is part of a larger body of work in Algorithm, is frequently linked to Noise, bridging the gap between disciplines.
His Persistent homology study combines topics from a wide range of disciplines, such as Betti number, Inference and Topological data analysis.
He most often published in these fields:
- Artificial intelligence (41.40%)
- Pattern recognition (27.91%)
- Smoothing (19.53%)
What were the highlights of his more recent work (between 2018-2021)?
- Algorithm (16.74%)
- Artificial intelligence (41.40%)
- Smoothing (19.53%)
In recent papers he was focusing on the following fields of study:
His main research concerns Algorithm, Artificial intelligence, Smoothing, Pattern recognition and Persistent homology.
Moo K. Chung has included themes like Gaussian function, Polynomial, Dynamic functional connectivity and Cosine series in his Algorithm study.
His Artificial intelligence study typically links adjacent topics like Infimum and supremum.
The Smoothing study combines topics in areas such as Diffusion wavelets, Numerical stability, Heat equation, Manifold and Heat kernel.
His Pattern recognition research is multidisciplinary, relying on both Twin study, Resting state fMRI, Logistic regression and Functional brain.
He combines subjects such as Functional magnetic resonance imaging, Betti number, Inference and Topological data analysis with his study of Persistent homology.
Between 2018 and 2021, his most popular works were:
- Exact topological inference of the resting-state brain networks in twins. (19 citations)
- Fast Polynomial Approximation of Heat Kernel Convolution on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis (8 citations)
- Altered dynamic electroencephalography connectome phase-space features of emotion regulation in social anxiety. (6 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Mathematical analysis
Persistent homology, Algorithm, Connected component, Resting state fMRI and Statistical inference are his primary areas of study.
The study incorporates disciplines such as Betti number, Topological data analysis, Artificial intelligence and Pattern recognition in addition to Persistent homology.
His Algorithm research includes elements of Smoothing, Algebraic structure, Parametric statistics and Permutation.
His Smoothing research incorporates elements of Regularization, Gaussian function, Heat kernel and Chebyshev polynomials.
His research investigates the connection between Connected component and topics such as Inference that intersect with issues in Brain network, Robustness and Topology.
His research in Resting state fMRI intersects with topics in Functional magnetic resonance imaging, Series, Functional brain and Human brain.
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