Markus L. Schmid

503 citations
34 papers · 104 · h-index 7

Impact in

Papers in

Markus L. Schmid

28 papers receiving 97 citations

Peers

Markus L. Schmid
Comparison fields: 5 of 19
  • Computational Theory and Mathematics 64
  • Artificial Intelligence 76
  • Hardware and Architecture 8
  • Software 4
  • Computer Graphics and Computer-Aided Design 2
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Takayoshi Shoudai Japan
Jean-Marc Talbot France
Luı́s Cruz-Filipe Denmark
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Avivit Levy Israel
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Hiroki Shizuya Japan
Claudio Ferretti Italy
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Citations per year

Countries citing papers authored by Markus L. Schmid

Since Specialization
Citations

This map shows the geographic impact of Markus L. Schmid'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 Markus L. Schmid with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Markus L. Schmid more than expected).

Fields of papers citing papers by Markus L. Schmid

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Markus L. Schmid. 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 Markus L. Schmid. The network helps show where Markus L. Schmid may publish in the future.

Co-authors

The 18 scholars most cited alongside Markus L. Schmid, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Markus L. Schmid Line = papers co-authored together Markus L. Schmid links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201613
2 201510
3 20159
4 20139
5 20148
6 20216
7 20156
8 20164
9 20133
10 20173
11 20203
12 20183
13 20183
14 20133
15 20213
16 20202
17 20142
18 20132
19 20162
20 20132

About Markus L. Schmid

Markus L. Schmid is a scholar working on Computational Theory and Mathematics, Artificial Intelligence, Molecular Biology, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 34 papers that have together received 104 indexed citations. Recurring topics across this work include semigroups and automata theory (22 papers), Algorithms and Data Compression (20 papers), DNA and Biological Computing (13 papers), Machine Learning and Algorithms (8 papers), Advanced Graph Theory Research (6 papers), Graph Theory and Algorithms (4 papers), Network Packet Processing and Optimization (3 papers) and Optimization and Search Problems (3 papers). The work is most often cited by research in Computational Theory and Mathematics (64 citations), Artificial Intelligence (76 citations), Hardware and Architecture (8 citations), Software (4 citations) and Computer Graphics and Computer-Aided Design (2 citations). Markus L. Schmid has collaborated with scholars based in Germany, United Kingdom and France. Frequent co-authors include Henning Fernau, Daniel Reidenbach, Yngve Villanger, Florín Manea, Robert Mercaş, Nicole Schweikardt, Rudolf Freund, K. G. Subramanian, Serge Gaspers and Laurent Bulteau. Their work appears in journals such as Theoretical Computer Science, Information and Computation, Theory of Computing Systems, Journal of Computer and System Sciences and Discrete Applied Mathematics.

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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