Sicco Verwer
Impact in
- Health Informatics top 5%
- Safety Research top 2%
- Ethics and Social Impacts of AI
Papers in
-
- Machine Learning and Algorithms 22
- Machine Learning and Data Classification 6
- Internet Traffic Analysis and Secure E-voting 5
-
- Formal Methods in Verification 12
- semigroups and automata theory 8
- Co-authors
- Toon Calders (1 shared paper)Qin Lin (10 shared papers)Yingqian Zhang (5 shared papers)Yihuan Zhang (4 shared papers)Jun Wang (4 shared papers)Aditya P. Mathur (1 shared paper)Mathijs de Weerdt (11 shared papers)Cees Witteveen (8 shared papers)
- Journals
- Machine Learning (3 papers)Theoretical Computer Science (1 paper)IEEE Transactions on Intelligent Vehicles (1 paper)Information Sciences (1 paper)Data Mining and Knowledge Discovery (1 paper)
- Partner nations
- NetherlandsLuxembourgUnited States
In The Last Decade
Sicco Verwer
59 papers receiving 1.1k citations
Sicco Verwer's Hit Papers
Peers
Comparison fields: 5 of 89
- Health Informatics 44
- Safety Research 228
- Software 84
- Artificial Intelligence 637
- Computer Networks and Communications 245
Countries citing papers authored by Sicco Verwer
This map shows the geographic impact of Sicco Verwer'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 Sicco Verwer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sicco Verwer more than expected).
Fields of papers citing papers by Sicco Verwer
This network shows the impact of papers produced by Sicco Verwer. 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 Sicco Verwer. The network helps show where Sicco Verwer may publish in the future.
Co-authors
The 25 scholars most cited alongside Sicco Verwer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 62 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Three naive Bayes approaches for discrimination-free classification Hit paper breakdown → | 2010 | 391 |
| 2 | 2018 | 94 | |
| 3 | 2018 | 86 | |
| 4 | 2019 | 75 | |
| 5 | Efficient Identification of Timed Automata: Theory and practice | 2010 | 40 |
| 6 | 2021 | 36 | |
| 7 | 2012 | 34 | |
| 8 | 2015 | 32 | |
| 9 | 2023 | 30 | |
| 10 | 2018 | 20 | |
| 11 | 2013 | 16 | |
| 12 | 2017 | 15 | |
| 13 | 2014 | 14 | |
| 14 | 2011 | 14 | |
| 15 | 2011 | 13 | |
| 16 | 2010 | 12 | |
| 17 | 2012 | 11 | |
| 18 | 2016 | 10 | |
| 19 | 2017 | 10 | |
| 20 | 2013 | 9 |
About Sicco Verwer
Sicco Verwer is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications, Software and Information Systems, having authored 62 papers that have together received 1.1k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (22 papers), Formal Methods in Verification (12 papers), Network Security and Intrusion Detection (11 papers), Software Testing and Debugging Techniques (8 papers), semigroups and automata theory (8 papers), Advanced Malware Detection Techniques (7 papers), Machine Learning and Data Classification (6 papers) and Internet Traffic Analysis and Secure E-voting (5 papers). The work is most often cited by research in Health Informatics (44 citations), Safety Research (228 citations), Software (84 citations), Artificial Intelligence (637 citations) and Computer Networks and Communications (245 citations). Sicco Verwer has collaborated with scholars based in Netherlands, Luxembourg and United States. Frequent co-authors include Toon Calders, Qin Lin, Yingqian Zhang, Yihuan Zhang, Jun Wang, Aditya P. Mathur, Mathijs de Weerdt, Cees Witteveen, John M. Dolan and Marijn J. H. Heule. Their work appears in journals such as Machine Learning, Theoretical Computer Science, IEEE Transactions on Intelligent Vehicles, Information Sciences and Data Mining and Knowledge Discovery.
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.