Jakub Mareček
- Artificial Intelligence top 5%
- Management Science and Operations Research top 5%
- Computer Networks and Communications top 10%
- Industrial and Manufacturing Engineering top 5%
- Electrical and Electronic Engineering
- Co-authors
- Stefan WoernerDaniel J. EggerEdmund BurkeAndrew ParkesHana RudováMartin MevissenBissan GhaddarJulien Monteil
- Topics
- Sparse and Compressive Sensing Techniques (4 papers)Quantum Computing Algorithms and Architecture (4 papers)Constraint Satisfaction and Optimization (4 papers)
- Cited by
- Industrial and Manufacturing EngineeringManagement Science and Operations ResearchArtificial Intelligence
- Partner nations
- CzechiaUnited KingdomIreland
In The Last Decade
Jakub Mareček
37 papers receiving 675 citations
Peers
Comparison fields: 5 of 88
- Artificial Intelligence 241
- Management Science and Operations Research 156
- Computer Networks and Communications 152
- Industrial and Manufacturing Engineering 126
- Electrical and Electronic Engineering 119
Countries citing papers authored by Jakub Mareček
This map shows the geographic impact of Jakub Mareček'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 Jakub Mareček with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jakub Mareček more than expected).
Fields of papers citing papers by Jakub Mareček
This network shows the impact of papers produced by Jakub Mareček. 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 Jakub Mareček. The network helps show where Jakub Mareček may publish in the future.
Co-authorship network of co-authors of Jakub Mareček
This figure shows the co-authorship network connecting the top 25 collaborators of Jakub Mareček. A scholar is included among the top collaborators of Jakub Mareček 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 Jakub Mareček. Jakub Mareček 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 | 0 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 3 | |
| 10 | 3 | |
| 11 | 8 | |
| 12 | 0 | |
| 13 | 2 | |
| 14 | 7 | |
| 15 | 151 | |
| 16 | 39 | |
| 17 | 3 | |
| 18 | Scaling up Deep Learning for PDE-based Models | 2 |
| 19 | 56 | |
| 20 | 57 |
About Jakub Mareček
Jakub Mareček is a scholar working on Numerical Analysis, Management Science and Operations Research and General Decision Sciences, having authored 45 papers that have together received 693 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (4 papers), Quantum Computing Algorithms and Architecture (4 papers) and Constraint Satisfaction and Optimization (4 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (126 citations), Management Science and Operations Research (156 citations) and Artificial Intelligence (241 citations). Jakub Mareček has collaborated with scholars based in Czechia, United Kingdom and Ireland. Frequent co-authors include Stefan Woerner, Daniel J. Egger, Edmund Burke, Andrew Parkes, Hana Rudová, Martin Mevissen, Bissan Ghaddar, Julien Monteil, Robert Shorten and Fearghal O’Donncha. Their work appears in journals such as PLoS ONE, IEEE Transactions on Automatic Control and Journal of Computational Physics.
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.