Thorsten Suttorp

459 citations
5 papers · 200 indexed · h-index 3
Topics
Metaheuristic Optimization Algorithms Research (2 papers)Advanced Multi-Objective Optimization Algorithms (2 papers)Evolutionary Algorithms and Applications (2 papers)
Journals
Machine LearningThe European Symposium on Artificial Neural Networks
Partner nations
GermanySwitzerlandFrance

In The Last Decade

Thorsten Suttorp

5 papers receiving 188 citations

Peers

Thorsten Suttorp
Comparison fields: 5 of 56
  • Artificial Intelligence 145
  • Computational Theory and Mathematics 80
  • Computer Vision and Pattern Recognition 27
  • Control and Systems Engineering 16
  • Management Science and Operations Research 14
Replace Christian W. G. Lasarczyk with:
Christian W. G. Lasarczyk Germany
Amir‐massoud Farahmand United States
Frank Vavak United Kingdom
Yi-Qi Hu China
Thang D. Bui United Kingdom
Klaus Meer Germany
Rahul Joshi India
Edward Meeds Netherlands
Shaaban Sahmoud Türkiye
Timothy Mann United States
Thorsten Suttorp relative to Christian W. G. Lasarczyk Germany Christian W. G. Lasarczyk's profile →
Citations per field
00.5×
Christian W. G. Lasarczyk · 1×
Citations per year

Countries citing papers authored by Thorsten Suttorp

Since Specialization
Citations

This map shows the geographic impact of Thorsten Suttorp's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Thorsten Suttorp with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thorsten Suttorp more than expected).

Fields of papers citing papers by Thorsten Suttorp

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thorsten Suttorp. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Thorsten Suttorp. The network helps show where Thorsten Suttorp may publish in the future.

Co-authorship network of co-authors of Thorsten Suttorp

This figure shows the co-authorship network connecting the top 25 collaborators of Thorsten Suttorp. A scholar is included among the top collaborators of Thorsten Suttorp based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Thorsten Suttorp. Thorsten Suttorp is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

5 of 5 papers shown
#WorkIndexed citations
1 71
2
Approximation of Gaussian process regression models after training.
2
3 2
4 111
5 14

About Thorsten Suttorp

Thorsten Suttorp is a scholar working on Signal Processing, Computational Theory and Mathematics and Artificial Intelligence, having authored 5 papers that have together received 200 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (2 papers), Advanced Multi-Objective Optimization Algorithms (2 papers) and Evolutionary Algorithms and Applications (2 papers). The work is most often cited by research in Computational Theory and Mathematics (80 citations), Artificial Intelligence (145 citations) and Computer Vision and Pattern Recognition (27 citations). Thorsten Suttorp has collaborated with scholars based in Germany, Switzerland and France. Frequent co-authors include Christian Igel, Nikolaus Hansen, Hans Ulrich Simon, Jürgen Forster, Jan Salmen and Johann Edelbrunner. Their work appears in journals such as Machine Learning and The European Symposium on Artificial Neural Networks.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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