What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Statistics
Ivan W. Selesnick focuses on Wavelet, Algorithm, Wavelet transform, Discrete wavelet transform and Wavelet packet decomposition.
His Wavelet study necessitates a more in-depth grasp of Artificial intelligence.
The Artificial intelligence study combines topics in areas such as Computer vision and Pattern recognition.
His work deals with themes such as Control theory, Estimator, Convex optimization, Fault and Signal, which intersect with Algorithm.
His Wavelet transform research is multidisciplinary, relying on both Orthonormal basis, Speech recognition and Filter bank.
Ivan W. Selesnick works on Wavelet packet decomposition which deals in particular with Stationary wavelet transform.
His most cited work include:
- The dual-tree complex wavelet transform (1927 citations)
- Introduction to Wavelets and Wavelet Transforms: A Primer (1429 citations)
- Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency (917 citations)
What are the main themes of his work throughout his whole career to date?
Ivan W. Selesnick mainly investigates Algorithm, Wavelet, Wavelet transform, Artificial intelligence and Mathematical optimization.
The various areas that he examines in his Algorithm study include Signal, Noise reduction, Piecewise and Convex optimization.
His Wavelet research incorporates elements of Discrete mathematics and Filter bank.
Ivan W. Selesnick combines subjects such as Image processing, Speech recognition, Mathematical analysis and Signal processing with his study of Wavelet transform.
His Artificial intelligence research is multidisciplinary, incorporating elements of Computer vision and Pattern recognition.
As part of the same scientific family, Ivan W. Selesnick usually focuses on Mathematical optimization, concentrating on Applied mathematics and intersecting with Finite impulse response.
He most often published in these fields:
- Algorithm (35.71%)
- Wavelet (28.57%)
- Wavelet transform (25.71%)
What were the highlights of his more recent work (between 2015-2021)?
- Algorithm (35.71%)
- Convex optimization (12.50%)
- Regularization (8.57%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Algorithm, Convex optimization, Regularization, Mathematical optimization and Noise reduction.
In the subject of general Algorithm, his work in Sparse approximation is often linked to Convexity, thereby combining diverse domains of study.
His research in Convex optimization focuses on subjects like Signal, which are connected to Artificial intelligence, Pattern recognition and Transient.
His study in Regularization is interdisciplinary in nature, drawing from both Signal reconstruction, Matrix norm and Regular polygon.
His study looks at the intersection of Mathematical optimization and topics like Convex function with Applied mathematics, Proximal gradient methods for learning and Linear programming.
In his research, Audio signal, Wavelet transform and Wavelet is intimately related to Intelligibility, which falls under the overarching field of Noise reduction.
Between 2015 and 2021, his most popular works were:
- Sparse Regularization via Convex Analysis (117 citations)
- Sparsity-based algorithm for detecting faults in rotating machines (88 citations)
- Nonconvex Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis (84 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
His scientific interests lie mostly in Convex optimization, Algorithm, Mathematical optimization, Convex function and Convexity.
His work carried out in the field of Convex optimization brings together such families of science as Regularization, Signal, Noise and Matrix norm.
His Signal research focuses on Piecewise and how it connects with Sleep spindle, Artificial intelligence, Communication channel and Electroencephalography.
His research on Algorithm focuses in particular on Optimization problem.
His Mathematical optimization study combines topics in areas such as Sparse matrix, Sparse approximation and Convex analysis.
His research in Convex function intersects with topics in Linear programming, Penalty method and Proximal gradient methods for learning.
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