Pavel Gurevich
- Human-Computer Interaction top 2%
- Computer Vision and Pattern Recognition top 5%
- Immunology
- Molecular Biology
- Applied Mathematics top 5%
- Topics
- Differential Equations and Boundary Problems (13 papers)Advanced Mathematical Modeling in Engineering (12 papers)Cancer Research and Treatments (9 papers)
In The Last Decade
Pavel Gurevich
75 papers receiving 840 citations
Peers
Comparison fields: 5 of 139
- Human-Computer Interaction 171
- Computer Vision and Pattern Recognition 162
- Immunology 136
- Molecular Biology 117
- Applied Mathematics 78
Countries citing papers authored by Pavel Gurevich
This map shows the geographic impact of Pavel Gurevich'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 Pavel Gurevich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pavel Gurevich more than expected).
Fields of papers citing papers by Pavel Gurevich
This network shows the impact of papers produced by Pavel Gurevich. 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 Pavel Gurevich. The network helps show where Pavel Gurevich may publish in the future.
Co-authorship network of co-authors of Pavel Gurevich
This figure shows the co-authorship network connecting the top 25 collaborators of Pavel Gurevich. A scholar is included among the top collaborators of Pavel Gurevich 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 Pavel Gurevich. Pavel Gurevich is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | Learning uncertainty in regression tasks by deep neural networks. | 1 |
| 3 | 34 | |
| 4 | 1 | |
| 5 | 5 | |
| 6 | 11 | |
| 7 | 6 | |
| 8 | 2 | |
| 9 | 10 | |
| 10 | 3 | |
| 11 | 6 | |
| 12 | 6 | |
| 13 | 1 | |
| 14 | 0 | |
| 15 | 4 | |
| 16 | 24 | |
| 17 | 41 | |
| 18 | Apoptosis and apoptosis-related proteins in the epithelium of human ovarian tumors: immunohistochemical and morphometric studies. | 18 |
| 19 | IgG generated against benign tumor-associated antigens prevented the effects of 1,2-dimethylhydrazine in rats. | 1 |
| 20 | 7 |
About Pavel Gurevich
Pavel Gurevich is a scholar working on Numerical Analysis, Applied Mathematics and Biotechnology, having authored 81 papers that have together received 886 indexed citations. Recurring topics across this work include Differential Equations and Boundary Problems (13 papers), Advanced Mathematical Modeling in Engineering (12 papers) and Cancer Research and Treatments (9 papers). The work is most often cited by research in Human-Computer Interaction (171 citations), Numerical Analysis (63 citations) and Obstetrics and Gynecology (68 citations). Pavel Gurevich has collaborated with scholars based in Israel, Russia and Germany. Frequent co-authors include Benjamin Cohen, Joel Lanir, Itshak Zusman, Herzl Ben‐Hur, Aharon Oren, Y. Henis, I. Zusman, Y Tendler, Asher Elhayany and David Schneider. Their work appears in journals such as Journal of Clinical Oncology, Applied and Environmental Microbiology and Journal of Differential Equations.
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.