Jan Vahrenhold
- Computer Science Applications top 1%
- Artificial Intelligence top 5%
- Developmental and Educational Psychology top 5%
- Computational Theory and Mathematics top 5%
- Signal Processing top 5%
- Co-authors
- Wolfgang J. PaulCarlos M. FonsecaLuís PaqueteManuel López‐IbáñezNicola BeumeLars ArgeLaura TomaK. Hinrichs
- Topics
- Teaching and Learning Programming (30 papers)Data Management and Algorithms (19 papers)Computational Geometry and Mesh Generation (16 papers)
- Cited by
- Computer Science ApplicationsComputer Graphics and Computer-Aided DesignDevelopmental and Educational Psychology
- Partner nations
- GermanyUnited StatesCanada
In The Last Decade
Jan Vahrenhold
70 papers receiving 837 citations
Peers
Comparison fields: 5 of 90
- Computer Science Applications 341
- Artificial Intelligence 240
- Developmental and Educational Psychology 173
- Computational Theory and Mathematics 171
- Signal Processing 137
Countries citing papers authored by Jan Vahrenhold
This map shows the geographic impact of Jan Vahrenhold'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 Jan Vahrenhold with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan Vahrenhold more than expected).
Fields of papers citing papers by Jan Vahrenhold
This network shows the impact of papers produced by Jan Vahrenhold. 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 Jan Vahrenhold. The network helps show where Jan Vahrenhold may publish in the future.
Co-authorship network of co-authors of Jan Vahrenhold
This figure shows the co-authorship network connecting the top 25 collaborators of Jan Vahrenhold. A scholar is included among the top collaborators of Jan Vahrenhold 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 Jan Vahrenhold. Jan Vahrenhold is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 15 | |
| 6 | 7 | |
| 7 | ICILS 2018 #Deutschland | 1 |
| 8 | 2 | |
| 9 | On the Space Efficiency of the "Ultimate Planar Convex Hull Algorithm". | 1 |
| 10 | 1 | |
| 11 | 20 | |
| 12 | Approximating Geodesic Distances on 2-Manifolds in R3. | 0 |
| 13 | 1 | |
| 14 | 1 | |
| 15 | 6 | |
| 16 | 17 | |
| 17 | 7 | |
| 18 | 6 | |
| 19 | 22 | |
| 20 | 6 |
About Jan Vahrenhold
Jan Vahrenhold is a scholar working on Computer Science Applications, Computer Graphics and Computer-Aided Design and Signal Processing, having authored 74 papers that have together received 874 indexed citations. Recurring topics across this work include Teaching and Learning Programming (30 papers), Data Management and Algorithms (19 papers) and Computational Geometry and Mesh Generation (16 papers). The work is most often cited by research in Computer Science Applications (341 citations), Computer Graphics and Computer-Aided Design (65 citations) and Developmental and Educational Psychology (173 citations). Jan Vahrenhold has collaborated with scholars based in Germany, United States and Canada. Frequent co-authors include Wolfgang J. Paul, Carlos M. Fonseca, Luís Paquete, Manuel López‐Ibáñez, Nicola Beume, Lars Arge, Laura Toma, K. Hinrichs, Jeffrey Scott Vitter and Andreas Thom. Their work appears in journals such as Computer, IEEE Transactions on Evolutionary Computation and Journal of Neuroscience Methods.
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.