What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
Stefan Wrobel focuses on Artificial intelligence, Machine learning, Visual analytics, Visualization and Data science.
His Artificial intelligence research is multidisciplinary, relying on both Transformation and Transformation based learning.
His studies deal with areas such as Algorithm, Information extraction and Graph as well as Machine learning.
His biological study spans a wide range of topics, including Event, Trajectory, Data visualization and Cluster analysis.
His work deals with themes such as Conceptual framework and Global Positioning System, which intersect with Data science.
His Kernel method study integrates concerns from other disciplines, such as Adjacency matrix, Cograph, 1-planar graph and Hamiltonian path.
His most cited work include:
- On Graph Kernels: Hardness Results and Efficient Alternatives (680 citations)
- An Algorithm for Multi-relational Discovery of Subgroups (464 citations)
- Geovisual analytics for spatial decision support: Setting the research agenda (326 citations)
What are the main themes of his work throughout his whole career to date?
Stefan Wrobel mostly deals with Artificial intelligence, Machine learning, Visual analytics, Data science and Data mining.
His Artificial intelligence research incorporates themes from Statistical relational learning and Pattern recognition.
The study of Machine learning is intertwined with the study of Transformation in a number of ways.
Visual analytics is a subfield of Visualization that Stefan Wrobel studies.
He combines subjects such as Field and Information visualization with his study of Data science.
The concepts of his Data mining study are interwoven with issues in Trajectory and Data set.
He most often published in these fields:
- Artificial intelligence (37.97%)
- Machine learning (27.27%)
- Visual analytics (18.18%)
What were the highlights of his more recent work (between 2017-2021)?
- Visual analytics (18.18%)
- Artificial intelligence (37.97%)
- Data science (13.90%)
In recent papers he was focusing on the following fields of study:
Stefan Wrobel spends much of his time researching Visual analytics, Artificial intelligence, Data science, Machine learning and Visualization.
Stefan Wrobel has researched Visual analytics in several fields, including Theoretical computer science, Pairwise comparison, Workflow and Human–computer interaction.
His Deep learning, Reinforcement learning and Artificial neural network study in the realm of Artificial intelligence interacts with subjects such as Automation and Selection.
His studies in Data science integrate themes in fields like Network embedding, Patent citation, Citation, Technological evolution and Software.
His study on Machine learning is mostly dedicated to connecting different topics, such as Data visualization.
He interconnects Topic model, Embedding, Data processing and Perception in the investigation of issues within Visualization.
Between 2017 and 2021, his most popular works were:
- Efficient Decentralized Deep Learning by Dynamic Model Averaging (53 citations)
- A review of machine learning for the optimization of production processes (28 citations)
- A theoretical model for pattern discovery in visual analytics (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
His primary areas of investigation include Artificial intelligence, Visual analytics, Data space, Word and Information retrieval.
In Artificial intelligence, he works on issues like Machine learning, which are connected to Data visualization.
His study brings together the fields of Data science and Visual analytics.
His Word research integrates issues from Range, Visualization and Workflow.
His work carried out in the field of Deep learning brings together such families of science as Contextual image classification, Reduction, Distributed computing and Code.
His work carried out in the field of Reinforcement learning brings together such families of science as Stochastic optimization, Leverage and Parameterized complexity.
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