Martin Heß
- Molecular Biology top 10%
- Cancer Research top 10%
- Pathology and Forensic Medicine top 10%
- Oncology
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Hanspeter NaegeliDaniela GunzRichard D. KolodnerUrs SchwitterMario PetrettaNatalia P. LunevaNicholas E. GeacintovBernd Giese
- Topics
- Robotic Path Planning Algorithms (8 papers)DNA Repair Mechanisms (7 papers)Distributed Control Multi-Agent Systems (6 papers)
- Journals
- Proceedings of the National Academy of SciencesJournal of Biological ChemistryGastroenterology
- Partner nations
- GermanySwitzerlandUnited States
In The Last Decade
Martin Heß
29 papers receiving 938 citations
Peers
Comparison fields: 5 of 94
- Molecular Biology 715
- Cancer Research 216
- Pathology and Forensic Medicine 214
- Oncology 149
- Computer Vision and Pattern Recognition 99
Countries citing papers authored by Martin Heß
This map shows the geographic impact of Martin Heß'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 Martin Heß with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin Heß more than expected).
Fields of papers citing papers by Martin Heß
This network shows the impact of papers produced by Martin Heß. 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 Martin Heß. The network helps show where Martin Heß may publish in the future.
Co-authorship network of co-authors of Martin Heß
This figure shows the co-authorship network connecting the top 25 collaborators of Martin Heß. A scholar is included among the top collaborators of Martin Heß 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 Martin Heß. Martin Heß 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 | 17 | |
| 3 | 15 | |
| 4 | 1 | |
| 5 | 11 | |
| 6 | 5 | |
| 7 | 46 | |
| 8 | 11 | |
| 9 | 8 | |
| 10 | 7 | |
| 11 | Path planning for formations using global optimization with sparse grids | 1 |
| 12 | Autonomous Multi-vehicle Formations for Cooperative Airfield Snow Shoveling. | 4 |
| 13 | 103 | |
| 14 | 57 | |
| 15 | 24 | |
| 16 | 82 | |
| 17 | 11 | |
| 18 | 12 | |
| 19 | 13 | |
| 20 | 170 |
About Martin Heß
Martin Heß is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications and Molecular Biology, having authored 30 papers that have together received 968 indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (8 papers), DNA Repair Mechanisms (7 papers) and Distributed Control Multi-Agent Systems (6 papers). The work is most often cited by research in Cancer Research (216 citations), Pathology and Forensic Medicine (214 citations) and Molecular Biology (715 citations). Martin Heß has collaborated with scholars based in Germany, Switzerland and United States. Frequent co-authors include Hanspeter Naegeli, Daniela Gunz, Richard D. Kolodner, Urs Schwitter, Mario Petretta, Natalia P. Luneva, Nicholas E. Geacintov, Bernd Giese, John M. Carethers and Betty L. Cabrera. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Gastroenterology.
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.