What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Machine learning
His main research concerns Artificial intelligence, Curvelet, Algorithm, Signal processing and Computer vision.
His studies deal with areas such as Geophysical imaging and Pattern recognition as well as Artificial intelligence.
His Curvelet study incorporates themes from Data compression and Noise reduction.
His studies in Signal processing integrate themes in fields like Machine learning, Mathematical optimization, Rank and Interpolation.
His Computer vision study combines topics from a wide range of disciplines, such as Remote sensing and Compressed sensing.
Jianwei Ma studied Wavelet and K-SVD that intersect with Sparse matrix, Representation, Noise and Seismic noise.
His most cited work include:
- The Curvelet Transform (283 citations)
- Double Sparsity Dictionary for Seismic Noise Attenuation (125 citations)
- Combined Curvelet Shrinkage and Nonlinear Anisotropic Diffusion (120 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Algorithm, Curvelet, Noise reduction and Wavelet.
He has included themes like Computer vision and Pattern recognition in his Artificial intelligence study.
His Algorithm research incorporates themes from Attenuation and Projection.
His Curvelet research is within the category of Wavelet transform.
His Noise reduction study combines topics in areas such as Deconvolution, Mathematical analysis, Signal-to-noise ratio, Seismic noise and Noise.
The Wavelet study which covers Signal processing that intersects with Fourier transform, Mathematical optimization, Interpolation and Machine learning.
He most often published in these fields:
- Artificial intelligence (46.49%)
- Algorithm (40.35%)
- Curvelet (35.96%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (46.49%)
- Deep learning (12.28%)
- Geophysics (4.39%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Deep learning, Geophysics, Noise reduction and Algorithm.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Pattern recognition.
His research integrates issues of Scattering, Inverse problem, Refractive index, Tomography and Projector in his study of Deep learning.
His studies deal with areas such as Transfer of learning, Data-driven and Current as well as Geophysics.
His biological study spans a wide range of topics, including Signal-to-noise ratio, Sparse approximation, Residual and Thresholding.
His Algorithm research includes themes of Seismic noise, Noise measurement, Noise and Noise.
Between 2019 and 2021, his most popular works were:
- Can learning from natural image denoising be used for seismic data interpolation (7 citations)
- Deep-learning projector for optical diffraction tomography. (3 citations)
- Data-driven geophysics: from dictionary learning to deep learning. (2 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Machine learning
The scientist’s investigation covers issues in Deep learning, Artificial intelligence, Set, Unsupervised learning and Applications of artificial intelligence.
His Artificial intelligence study typically links adjacent topics like Algorithm.
His Algorithm study deals with Tomography intersecting with Inverse problem and Scattering.
He incorporates a variety of subjects into his writings, including Set, Pattern recognition, Convolutional neural network, Reduction, Interpolation and Image.
His Pattern recognition research integrates issues from Noise reduction and Big data.
His Unsupervised learning research is multidisciplinary, incorporating elements of Data-driven, Geophysics and Curse of dimensionality.
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