What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Electrical engineering
Tom Dhaene spends much of his time researching Algorithm, Mathematical optimization, Electronic engineering, Surrogate model and Parameterized complexity.
His work in the fields of Multi-objective optimization overlaps with other areas such as Voronoi diagram.
Tom Dhaene has included themes like Stochastic process, Equivalent circuit, Nonlinear system and Polynomial chaos in his Electronic engineering study.
His Surrogate model research integrates issues from Design of experiments, Active learning, Artificial intelligence and Design space exploration.
His Parameterized complexity research incorporates elements of Passivity, Control theory, Parametric statistics and Interpolation.
His research in Parametric statistics intersects with topics in Frequency domain and Robustness.
His most cited work include:
- FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. (492 citations)
- A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design (372 citations)
- Macromodeling of Multiport Systems Using a Fast Implementation of the Vector Fitting Method (357 citations)
What are the main themes of his work throughout his whole career to date?
Tom Dhaene mainly investigates Algorithm, Electronic engineering, Mathematical optimization, Frequency domain and Artificial intelligence.
His Algorithm research incorporates themes from Transfer function, Parametric statistics, Interpolation, Frequency response and Scattering parameters.
In his study, Electric power transmission is strongly linked to Transmission line, which falls under the umbrella field of Electronic engineering.
His study explores the link between Mathematical optimization and topics such as Kriging that cross with problems in Engineering design process and Benchmark.
Tom Dhaene combines subjects such as Orthonormal basis and Control theory with his study of Frequency domain.
His research integrates issues of Machine learning, Data mining and Pattern recognition in his study of Artificial intelligence.
He most often published in these fields:
- Algorithm (27.22%)
- Electronic engineering (22.15%)
- Mathematical optimization (21.94%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (11.81%)
- Algorithm (27.22%)
- Electronic engineering (22.15%)
In recent papers he was focusing on the following fields of study:
Tom Dhaene mostly deals with Artificial intelligence, Algorithm, Electronic engineering, Gaussian process and Machine learning.
The concepts of his Artificial intelligence study are interwoven with issues in Data modeling, Trajectory and Pattern recognition.
Tom Dhaene has researched Algorithm in several fields, including Sampling, Stochastic process and Kernel.
His biological study spans a wide range of topics, including Photonics, Photonic integrated circuit, Electronic circuit, Time domain and Baseband.
His Machine learning study combines topics in areas such as Electromagnetic compatibility, Training set, Data point, Distortion and Signal.
His Response surface methodology research is multidisciplinary, relying on both Artificial neural network and Metamodeling.
Between 2016 and 2021, his most popular works were:
- Appliance classification using VI trajectories and convolutional neural networks (63 citations)
- Comprehensive feature selection for appliance classification in NILM (53 citations)
- Detection of unidentified appliances in non-intrusive load monitoring using siamese neural networks (36 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Electrical engineering
His primary scientific interests are in Data mining, Artificial intelligence, Polynomial chaos, Voltage and Mathematical optimization.
His Data mining research includes themes of Signature, Grid connection, Feature, Classifier and Set.
Tom Dhaene has included themes like Machine learning and Trajectory in his Artificial intelligence study.
His studies deal with areas such as Stochastic process and Nonlinear system as well as Polynomial chaos.
His Nonlinear system study integrates concerns from other disciplines, such as Uncertainty quantification, Electronic circuit and Electronic engineering.
His work in Mathematical optimization covers topics such as Algorithm which are related to areas like Numerical analysis.
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