What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Data mining, Cluster analysis, Graph and Theoretical computer science.
His work deals with themes such as Machine learning and Computation, which intersect with Artificial intelligence.
The concepts of his Data mining study are interwoven with issues in Correlation clustering and Fuzzy clustering.
Stephan Günnemann is studying Clustering high-dimensional data, which is a component of Cluster analysis.
His study looks at the relationship between Graph and topics such as Algorithm, which overlap with Attack model, Spectral clustering and Spectral method.
His Theoretical computer science research incorporates elements of Adversarial system, Embedding, Representation, Ranking and Random walk.
His most cited work include:
- Adversarial Attacks on Neural Networks for Graph Data (220 citations)
- Evaluating clustering in subspace projections of high dimensional data (190 citations)
- Pitfalls of Graph Neural Network Evaluation. (185 citations)
What are the main themes of his work throughout his whole career to date?
Stephan Günnemann mainly investigates Artificial intelligence, Data mining, Theoretical computer science, Graph and Cluster analysis.
He has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.
His Data mining research is multidisciplinary, incorporating perspectives in Scalability, Multivariate statistics and Tracing.
His Theoretical computer science research is multidisciplinary, incorporating elements of Adversarial system, Graph, Structure, Computation and Random walk.
Stephan Günnemann has included themes like Artificial neural network, Computational complexity theory, Algorithm and PageRank in his Graph study.
Stephan Günnemann combines subjects such as Subspace topology and Linear subspace with his study of Cluster analysis.
He most often published in these fields:
- Artificial intelligence (38.25%)
- Data mining (30.60%)
- Theoretical computer science (24.59%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (38.25%)
- Algorithm (12.57%)
- Robustness (13.66%)
In recent papers he was focusing on the following fields of study:
Stephan Günnemann mostly deals with Artificial intelligence, Algorithm, Robustness, Theoretical computer science and Graph.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Code and Natural language processing.
His Robustness research includes elements of Smoothing, Feature and Semi-supervised learning.
The study incorporates disciplines such as Relational database, Graph and Scaling in addition to Theoretical computer science.
His research in the fields of Graph neural networks overlaps with other disciplines such as Scene graph.
His work on Graph neural networks is being expanded to include thematically relevant topics such as Data mining.
Between 2019 and 2021, his most popular works were:
- Directional Message Passing for Molecular Graphs (54 citations)
- Scaling Graph Neural Networks with Approximate PageRank (24 citations)
- Directional Message Passing for Molecular Graphs (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Stephan Günnemann mainly focuses on Graph, Theoretical computer science, Algorithm, Message passing and Artificial neural network.
The Graph study combines topics in areas such as Graph and Artificial intelligence.
His research integrates issues of Basis and Natural language processing in his study of Artificial intelligence.
His studies deal with areas such as Relational database, Graph neural networks and Scaling as well as Theoretical computer science.
His biological study spans a wide range of topics, including Equivariant map and Topology.
His Artificial neural network study incorporates themes from Calibration, Uncertainty quantification, Inference, Posterior probability and Dropout.
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