V. A. Uspenskiĭ
- Computational Theory and Mathematics top 5%
- Artificial Intelligence
- Statistics and Probability top 5%
- Mathematical Physics
- Statistical and Nonlinear Physics
- Co-authors
- A. N. KolmogorovA. L. SemenovAlexander ShenАндрей Николаевич КолмогоровR. L. DobrushinСергей Иванович АдянE. B. DynkinA. L. Onishchik
- Topics
- Computability, Logic, AI Algorithms (8 papers)Benford’s Law and Fraud Detection (3 papers)History and Theory of Mathematics (3 papers)
- Journals
- Russian Mathematical SurveysTheory of Probability and Its ApplicationsJournal of Engineering Physics and Thermophysics
- Partner nations
- Russia
In The Last Decade
V. A. Uspenskiĭ
12 papers receiving 148 citations
Peers
Comparison fields: 5 of 50
- Computational Theory and Mathematics 143
- Artificial Intelligence 87
- Statistics and Probability 53
- Mathematical Physics 27
- Statistical and Nonlinear Physics 22
Countries citing papers authored by V. A. Uspenskiĭ
This map shows the geographic impact of V. A. Uspenskiĭ'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 V. A. Uspenskiĭ with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites V. A. Uspenskiĭ more than expected).
Fields of papers citing papers by V. A. Uspenskiĭ
This network shows the impact of papers produced by V. A. Uspenskiĭ. 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 V. A. Uspenskiĭ. The network helps show where V. A. Uspenskiĭ may publish in the future.
Co-authorship network of co-authors of V. A. Uspenskiĭ
This figure shows the co-authorship network connecting the top 25 collaborators of V. A. Uspenskiĭ. A scholar is included among the top collaborators of V. A. Uspenskiĭ 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 V. A. Uspenskiĭ. V. A. Uspenskiĭ is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Теорема Гёделя о неполноте и четыре дороги, ведущие к ней | 1 |
| 2 | 2 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | Тайный советник вождя | 1 |
| 7 | 2 | |
| 8 | Математика в ее историческом развитии | 1 |
| 9 | 93 | |
| 10 | Высказывание и его соотнесенность с действительностью : референциальные аспекты семантики местоимений | 2 |
| 11 | 8 | |
| 12 | 2 | |
| 13 | Очерки по истории семиотики в СССР | 3 |
| 14 | Pascal's Triangle. Popular Lectures in Mathematics. | 1 |
| 15 | 3 | |
| 16 | 0 | |
| 17 | Leçons sur les fonctions calculables | 2 |
| 18 | 0 | |
| 19 | Problems in the theory of numbers | 4 |
| 20 | 0 |
About V. A. Uspenskiĭ
V. A. Uspenskiĭ is a scholar working on Theoretical Computer Science, Computational Theory and Mathematics and Statistics and Probability, having authored 24 papers that have together received 204 indexed citations. Recurring topics across this work include Computability, Logic, AI Algorithms (8 papers), Benford’s Law and Fraud Detection (3 papers) and History and Theory of Mathematics (3 papers). The work is most often cited by research in Computational Theory and Mathematics (143 citations), Theoretical Computer Science (8 citations) and Statistics and Probability (53 citations). V. A. Uspenskiĭ has collaborated with scholars based in Russia. Frequent co-authors include A. N. Kolmogorov, A. L. Semenov, Alexander Shen, Андрей Николаевич Колмогоров, R. L. Dobrushin, Сергей Иванович Адян, E. B. Dynkin, A. L. Onishchik, Мati Pentus and А. О. Гелъфонд. Their work appears in journals such as Russian Mathematical Surveys, Theory of Probability and Its Applications and Journal of Engineering Physics and Thermophysics.
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.