Laurent Bienvenu
- Computational Theory and Mathematics top 5%
- Artificial Intelligence
- Statistics and Probability top 5%
- Geometry and Topology top 10%
- Mathematical Physics
- Co-authors
- Alexander ShenMathieu HoyrupJoseph S. MillerAndré NiesWolfgang MerkleRod DowneyNoam GreenbergRupert Hölzl
- Topics
- Computability, Logic, AI Algorithms (34 papers)Benford’s Law and Fraud Detection (18 papers)semigroups and automata theory (15 papers)
- Journals
- Theoretical Computer ScienceJournal of Computer and System SciencesLecture notes in computer science
- Partner nations
- FranceUnited StatesNew Zealand
In The Last Decade
Laurent Bienvenu
30 papers receiving 144 citations
Peers
Comparison fields: 5 of 20
- Computational Theory and Mathematics 153
- Artificial Intelligence 58
- Statistics and Probability 56
- Geometry and Topology 39
- Mathematical Physics 32
Countries citing papers authored by Laurent Bienvenu
This map shows the geographic impact of Laurent Bienvenu'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 Laurent Bienvenu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Laurent Bienvenu more than expected).
Fields of papers citing papers by Laurent Bienvenu
This network shows the impact of papers produced by Laurent Bienvenu. 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 Laurent Bienvenu. The network helps show where Laurent Bienvenu may publish in the future.
Co-authorship network of co-authors of Laurent Bienvenu
This figure shows the co-authorship network connecting the top 25 collaborators of Laurent Bienvenu. A scholar is included among the top collaborators of Laurent Bienvenu 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 Laurent Bienvenu. Laurent Bienvenu 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 | 2 | |
| 3 | 2 | |
| 4 | 3 | |
| 5 | Pursuit of the Universal : 12th Conference on Computability in Europe, CiE 2016, Paris, France, June 27 - July 1, 2016, Proceedings | 3 |
| 6 | 0 | |
| 7 | 6 | |
| 8 | 3 | |
| 9 | 0 | |
| 10 | A Ramsey-Type K\"onig's lemma and its variants | 1 |
| 11 | 4 | |
| 12 | 9 | |
| 13 | 8 | |
| 14 | 9 | |
| 15 | 7 | |
| 16 | 19 | |
| 17 | 6 | |
| 18 | 6 | |
| 19 | 3 | |
| 20 | A Simple Proof of Miller-Yu Theorem | 1 |
About Laurent Bienvenu
Laurent Bienvenu is a scholar working on Statistics and Probability, Computational Theory and Mathematics and Mathematical Physics, having authored 36 papers that have together received 161 indexed citations. Recurring topics across this work include Computability, Logic, AI Algorithms (34 papers), Benford’s Law and Fraud Detection (18 papers) and semigroups and automata theory (15 papers). The work is most often cited by research in Computational Theory and Mathematics (153 citations), Statistics and Probability (56 citations) and Geometry and Topology (39 citations). Laurent Bienvenu has collaborated with scholars based in France, United States and New Zealand. Frequent co-authors include Alexander Shen, Mathieu Hoyrup, Joseph S. Miller, André Nies, Wolfgang Merkle, Rod Downey, Noam Greenberg, Rupert Hölzl, Frank Stephan and Péter Gács. Their work appears in journals such as Theoretical Computer Science, Journal of Computer and System Sciences and Lecture notes in 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.