Sergei Soloviev
- Cognitive Neuroscience top 10%
- Artificial Intelligence
- Computational Theory and Mathematics top 10%
- Mathematical Physics
- Geometry and Topology top 10%
- Co-authors
- Lucia M. VainaAlan CoweyZhaohui LuoRonald BrownPhilip J. HigginsRafael SiveraDon C. BienfangG. Longo
- Topics
- Logic, Reasoning, and Knowledge (11 papers)Logic, programming, and type systems (11 papers)Visual perception and processing mechanisms (4 papers)
- Partner nations
- FranceRussiaUnited Kingdom
In The Last Decade
Sergei Soloviev
18 papers receiving 205 citations
Peers
Comparison fields: 5 of 53
- Cognitive Neuroscience 106
- Artificial Intelligence 57
- Computational Theory and Mathematics 41
- Mathematical Physics 35
- Geometry and Topology 31
Countries citing papers authored by Sergei Soloviev
This map shows the geographic impact of Sergei Soloviev'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 Sergei Soloviev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sergei Soloviev more than expected).
Fields of papers citing papers by Sergei Soloviev
This network shows the impact of papers produced by Sergei Soloviev. 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 Sergei Soloviev. The network helps show where Sergei Soloviev may publish in the future.
Co-authorship network of co-authors of Sergei Soloviev
This figure shows the co-authorship network connecting the top 25 collaborators of Sergei Soloviev. A scholar is included among the top collaborators of Sergei Soloviev 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 Sergei Soloviev. Sergei Soloviev 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 | 1 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 18 | |
| 10 | Nonabelian Algebraic Topology: Filtered Spaces, Crossed Complexes, Cubical Homotopy Groupoids | 40 |
| 11 | 21 | |
| 12 | 49 | |
| 13 | 0 | |
| 14 | 32 | |
| 15 | 6 | |
| 16 | 4 | |
| 17 | 2 | |
| 18 | 2 | |
| 19 | 19 | |
| 20 | The Genericity Theorem and the Notion of Parametricity in the Polymorphic lambda-calculus (Extended Abstract) | 1 |
About Sergei Soloviev
Sergei Soloviev is a scholar working on Theoretical Computer Science, Algebra and Number Theory and Artificial Intelligence, having authored 24 papers that have together received 219 indexed citations. Recurring topics across this work include Logic, Reasoning, and Knowledge (11 papers), Logic, programming, and type systems (11 papers) and Visual perception and processing mechanisms (4 papers). The work is most often cited by research in Cognitive Neuroscience (106 citations), Mathematical Physics (35 citations) and Algebra and Number Theory (17 citations). Sergei Soloviev has collaborated with scholars based in France, Russia and United Kingdom. Frequent co-authors include Lucia M. Vaina, Alan Cowey, Zhaohui Luo, Ronald Brown, Philip J. Higgins, Rafael Sivera, Don C. Bienfang, G. Longo, Finnegan J. Calabro and Ferdinando S. Buonanno. Their work appears in journals such as Journal of Cognitive Neuroscience, Neuroreport and Progress in brain research.
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.