Yakov Nekrich
- Artificial Intelligence top 10%
- Molecular Biology
- Computer Vision and Pattern Recognition top 10%
- Signal Processing top 10%
- Computer Networks and Communications
- Co-authors
- Gonzalo NavarroMarek KarpińskiJeffrey Scott VitterJ. Ian MunroLuís M. S.RussoHongwei HuoFrancisco ClaudePiotr Berman
- Topics
- Algorithms and Data Compression (33 papers)DNA and Biological Computing (15 papers)Network Packet Processing and Optimization (11 papers)
- Partner nations
- United StatesCanadaGermany
In The Last Decade
Yakov Nekrich
36 papers receiving 225 citations
Peers
Comparison fields: 5 of 26
- Artificial Intelligence 169
- Molecular Biology 73
- Computer Vision and Pattern Recognition 71
- Signal Processing 70
- Computer Networks and Communications 49
Countries citing papers authored by Yakov Nekrich
This map shows the geographic impact of Yakov Nekrich'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 Yakov Nekrich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yakov Nekrich more than expected).
Fields of papers citing papers by Yakov Nekrich
This network shows the impact of papers produced by Yakov Nekrich. 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 Yakov Nekrich. The network helps show where Yakov Nekrich may publish in the future.
Co-authorship network of co-authors of Yakov Nekrich
This figure shows the co-authorship network connecting the top 25 collaborators of Yakov Nekrich. A scholar is included among the top collaborators of Yakov Nekrich 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 Yakov Nekrich. Yakov Nekrich is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 4 | |
| 5 | 9 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 16 | |
| 9 | 13 | |
| 10 | 5 | |
| 11 | 2 | |
| 12 | 5 | |
| 13 | 17 | |
| 14 | 20 | |
| 15 | 0 | |
| 16 | Approximating Range-Aggregate Queries using Coresets | 1 |
| 17 | 15 | |
| 18 | 7 | |
| 19 | 13 | |
| 20 | A Note on Traversing Skew Merkle Trees | 3 |
About Yakov Nekrich
Yakov Nekrich is a scholar working on Computer Graphics and Computer-Aided Design, Hardware and Architecture and Artificial Intelligence, having authored 43 papers that have together received 233 indexed citations. Recurring topics across this work include Algorithms and Data Compression (33 papers), DNA and Biological Computing (15 papers) and Network Packet Processing and Optimization (11 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (40 citations), Hardware and Architecture (44 citations) and Signal Processing (70 citations). Yakov Nekrich has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Gonzalo Navarro, Marek Karpiński, Jeffrey Scott Vitter, J. Ian Munro, Luís M. S.Russo, Hongwei Huo, Francisco Claude, Piotr Berman, Travis Gagie and Jérémy Barbay. Their work appears in journals such as SIAM Journal on Computing, Theoretical Computer Science and ACM Transactions on Database Systems.
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.