Jan Mulawka
- Artificial Intelligence top 10%
- Computational Theory and Mathematics top 5%
- Molecular Biology
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition
- Co-authors
- Jarosław ArabasZbigniew MichalewiczBrijesh VermaBogdan LesyngWitold R. RudnickiAndrzej PłucienniczakPiotr BorsukPiotr Węgleński
- Topics
- DNA and Biological Computing (9 papers)Advanced biosensing and bioanalysis techniques (9 papers)Analog and Mixed-Signal Circuit Design (9 papers)
- Cited by
- Computational Theory and MathematicsArtificial IntelligenceComputer Vision and Pattern Recognition
- Partner nations
- PolandUnited StatesAustralia
In The Last Decade
Jan Mulawka
29 papers receiving 315 citations
Peers
Comparison fields: 5 of 80
- Artificial Intelligence 178
- Computational Theory and Mathematics 106
- Molecular Biology 97
- Electrical and Electronic Engineering 45
- Computer Vision and Pattern Recognition 40
Countries citing papers authored by Jan Mulawka
This map shows the geographic impact of Jan Mulawka'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 Jan Mulawka with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan Mulawka more than expected).
Fields of papers citing papers by Jan Mulawka
This network shows the impact of papers produced by Jan Mulawka. 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 Jan Mulawka. The network helps show where Jan Mulawka may publish in the future.
Co-authorship network of co-authors of Jan Mulawka
This figure shows the co-authorship network connecting the top 25 collaborators of Jan Mulawka. A scholar is included among the top collaborators of Jan Mulawka 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 Jan Mulawka. Jan Mulawka 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 | 1 | |
| 4 | 2 | |
| 5 | 2 | |
| 6 | 5 | |
| 7 | 14 | |
| 8 | 0 | |
| 9 | Lab-on-a-chip molecular inference system | 1 |
| 10 | 1 | |
| 11 | 5 | |
| 12 | 6 | |
| 13 | Obliczenia molekularne - nowy kierunek technik informacyjnych | 1 |
| 14 | A new training algorithm for feedforward neural networks. | 3 |
| 15 | A New Class of the Crossover Operators for the Numerical Optimization | 4 |
| 16 | 2 | |
| 17 | 9 | |
| 18 | 1 | |
| 19 | 1 | |
| 20 | 1 |
About Jan Mulawka
Jan Mulawka is a scholar working on Software, Hardware and Architecture and Artificial Intelligence, having authored 41 papers that have together received 366 indexed citations. Recurring topics across this work include DNA and Biological Computing (9 papers), Advanced biosensing and bioanalysis techniques (9 papers) and Analog and Mixed-Signal Circuit Design (9 papers). The work is most often cited by research in Computational Theory and Mathematics (106 citations), Artificial Intelligence (178 citations) and Computer Vision and Pattern Recognition (40 citations). Jan Mulawka has collaborated with scholars based in Poland, United States and Australia. Frequent co-authors include Jarosław Arabas, Zbigniew Michalewicz, Brijesh Verma, Bogdan Lesyng, Witold R. Rudnicki, Andrzej Płucienniczak, Piotr Borsuk, Piotr Węgleński, G.S. Moschytz and Robert Nowak. Their work appears in journals such as Journal of the Franklin Institute, Electronics Letters and Future Generation Computer 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.