Michel de Rougemont
- Artificial Intelligence
- Computational Theory and Mathematics top 10%
- Computer Networks and Communications
- Signal Processing
- Management Science and Operations Research
- Co-authors
- Frédéric MagniezRichard LassaigneSylvain PeyronnetDavid Gross-AmblardE. FischerEldar FischerCarlos RodríguezChristoph Schlieder
- Topics
- Logic, Reasoning, and Knowledge (7 papers)Complexity and Algorithms in Graphs (7 papers)semigroups and automata theory (6 papers)
In The Last Decade
Michel de Rougemont
19 papers receiving 121 citations
Peers
Comparison fields: 5 of 31
- Artificial Intelligence 89
- Computational Theory and Mathematics 65
- Computer Networks and Communications 42
- Signal Processing 28
- Management Science and Operations Research 16
Countries citing papers authored by Michel de Rougemont
This map shows the geographic impact of Michel de Rougemont'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 Michel de Rougemont with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michel de Rougemont more than expected).
Fields of papers citing papers by Michel de Rougemont
This network shows the impact of papers produced by Michel de Rougemont. 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 Michel de Rougemont. The network helps show where Michel de Rougemont may publish in the future.
Co-authorship network of co-authors of Michel de Rougemont
This figure shows the co-authorship network connecting the top 25 collaborators of Michel de Rougemont. A scholar is included among the top collaborators of Michel de Rougemont 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 Michel de Rougemont. Michel de Rougemont 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 | 0 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 5 | |
| 6 | 4 | |
| 7 | Property and Equivalence Testing on Strings | 1 |
| 8 | 1 | |
| 9 | Definability and Compression | 1 |
| 10 | 1 | |
| 11 | Spatial navigation with uncertain deviations | 1 |
| 12 | 25 | |
| 13 | The Reliability of Queries. | 9 |
| 14 | 8 | |
| 15 | Fixed-point semantics and the representation of algorithms on large data | 0 |
| 16 | 39 | |
| 17 | Logic on finite structures and logic programming | 0 |
| 18 | From Logic to Logic Programming. | 1 |
| 19 | 0 | |
| 20 | 10 |
About Michel de Rougemont
Michel de Rougemont is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Signal Processing, having authored 28 papers that have together received 134 indexed citations. Recurring topics across this work include Logic, Reasoning, and Knowledge (7 papers), Complexity and Algorithms in Graphs (7 papers) and semigroups and automata theory (6 papers). The work is most often cited by research in Computational Theory and Mathematics (65 citations), Artificial Intelligence (89 citations) and Signal Processing (28 citations). Michel de Rougemont has collaborated with scholars based in France, Germany and Russia. Frequent co-authors include Frédéric Magniez, Richard Lassaigne, Sylvain Peyronnet, David Gross-Amblard, E. Fischer, Eldar Fischer, Carlos Rodríguez, Christoph Schlieder, Sophie Laplante and Divyakant Agrawal. Their work appears in journals such as SIAM Journal on Computing, Proceedings of the VLDB Endowment and Theoretical Computer Science.
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.