What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Algorithm, Artificial intelligence, Signal processing, Control theory and Nonlinear system.
His study in Algorithm is interdisciplinary in nature, drawing from both Scatter plot, Quaternion, Kernel adaptive filter and Benchmark.
His Artificial intelligence research incorporates elements of Machine learning, Hilbert–Huang transform, Speech recognition and Pattern recognition.
The concepts of his Signal processing study are interwoven with issues in Digital signal processing and Data mining.
His research integrates issues of Estimator and Linear model in his study of Control theory.
His Nonlinear system research includes themes of Recurrent neural network, Time series, Mathematical optimization, Extension and Range.
His most cited work include:
- Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (742 citations)
- Multivariate empirical mode decomposition (565 citations)
- Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability (515 citations)
What are the main themes of his work throughout his whole career to date?
Algorithm, Artificial intelligence, Adaptive filter, Control theory and Nonlinear system are his primary areas of study.
Linear model is closely connected to Quaternion in his research, which is encompassed under the umbrella topic of Algorithm.
Danilo P. Mandic has included themes like Machine learning, Electroencephalography and Pattern recognition in his Artificial intelligence study.
Danilo P. Mandic studied Pattern recognition and Hilbert–Huang transform that intersect with Speech recognition and Time–frequency analysis.
His biological study spans a wide range of topics, including Convergence, Algorithm design, Kernel adaptive filter and Finite impulse response.
His Control theory study incorporates themes from Estimator and Electric power system.
He most often published in these fields:
- Algorithm (31.60%)
- Artificial intelligence (23.26%)
- Adaptive filter (17.37%)
What were the highlights of his more recent work (between 2018-2021)?
- Algorithm (31.60%)
- Signal processing (13.68%)
- Theoretical computer science (4.24%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Algorithm, Signal processing, Theoretical computer science, Tensor and Artificial intelligence.
He is interested in Adaptive filter, which is a branch of Algorithm.
His Signal processing study combines topics from a wide range of disciplines, such as Digital signal processing, Graph theory, Data analysis and Restricted isometry property.
His work is dedicated to discovering how Theoretical computer science, Tensor are connected with Curse of dimensionality and other disciplines.
His research in Tensor tackles topics such as Space which are related to areas like Square and Computation.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition.
Between 2018 and 2021, his most popular works were:
- Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor (19 citations)
- Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion (15 citations)
- Tucker Tensor Layer in Fully Connected Neural Networks (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Algorithm, Signal processing, Tensor, Theoretical computer science and Graph.
His study in the field of Adaptive filter is also linked to topics like Wigner distribution function.
His studies deal with areas such as Graph theory and Data analysis as well as Signal processing.
His work carried out in the field of Tensor brings together such families of science as Matrix decomposition, Matrix, Approximation algorithm, Rank and Volatility.
His Graph research integrates issues from Recurrent neural network and Graph.
In his study, Hilbert–Huang transform and Multivariate statistics is inextricably linked to Pattern recognition, which falls within the broad field of Artificial intelligence.
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