Stefan Coors
- Artificial Intelligence top 10%
- Molecular Biology
- Environmental Engineering
- Electrical and Electronic Engineering
- Ecology
- Co-authors
- Bernd BischlMartin BinderMichel LangJakob RichterTobias PielokJanek ThomasDifan DengMarc Becker
- Topics
- Machine Learning and Data Classification (4 papers)Advanced Multi-Objective Optimization Algorithms (2 papers)Metaheuristic Optimization Algorithms Research (2 papers)
- Journals
- Investigative RadiologyEuropean Archives of Psychiatry and Clinical NeuroscienceWiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
- Partner nations
- GermanyUnited StatesSwitzerland
In The Last Decade
Stefan Coors
7 papers receiving 716 citations
Hit Papers
Peers
Comparison fields: 5 of 153
- Artificial Intelligence 200
- Molecular Biology 63
- Environmental Engineering 58
- Electrical and Electronic Engineering 56
- Ecology 41
Countries citing papers authored by Stefan Coors
This map shows the geographic impact of Stefan Coors'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 Stefan Coors with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Coors more than expected).
Fields of papers citing papers by Stefan Coors
This network shows the impact of papers produced by Stefan Coors. 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 Stefan Coors. The network helps show where Stefan Coors may publish in the future.
Co-authorship network of co-authors of Stefan Coors
This figure shows the co-authorship network connecting the top 25 collaborators of Stefan Coors. A scholar is included among the top collaborators of Stefan Coors 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 Stefan Coors. Stefan Coors is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 36 | |
| 3 | 13 | |
| 4 | Hyperparameter optimization: Foundations, algorithms, best practices, and open challengesbreakdown → | 398 |
| 5 | 49 | |
| 6 | 235 | |
| 7 | 7 |
About Stefan Coors
Stefan Coors is a scholar working on Applied Psychology, Computer Science Applications and Artificial Intelligence, having authored 7 papers that have together received 739 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (4 papers), Advanced Multi-Objective Optimization Algorithms (2 papers) and Metaheuristic Optimization Algorithms Research (2 papers). The work is most often cited by research in Health Informatics (12 citations), Computer Science Applications (36 citations) and Artificial Intelligence (200 citations). Stefan Coors has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Bernd Bischl, Martin Binder, Michel Lang, Jakob Richter, Tobias Pielok, Janek Thomas, Difan Deng, Marc Becker, Marius Lindauer and Theresa Ullmann. Their work appears in journals such as Investigative Radiology, European Archives of Psychiatry and Clinical Neuroscience and Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery.
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.