Dominik Olszewski
- Artificial Intelligence top 10%
- Information Systems top 10%
- Computer Vision and Pattern Recognition
- Accounting
- Computer Networks and Communications
- Co-authors
- Marcin IwanowskiMarcin KołodziejGerardo TurcattiFabien KuttlerMichael BauerVardan AndriasyanFanny GeorgiUrs F. Greber
- Topics
- Face and Expression Recognition (6 papers)Advanced Clustering Algorithms Research (6 papers)Neural Networks and Applications (4 papers)
- Partner nations
- PolandSwitzerlandSlovenia
In The Last Decade
Dominik Olszewski
14 papers receiving 211 citations
Peers
Comparison fields: 5 of 62
- Artificial Intelligence 173
- Information Systems 49
- Computer Vision and Pattern Recognition 41
- Accounting 36
- Computer Networks and Communications 28
Countries citing papers authored by Dominik Olszewski
This map shows the geographic impact of Dominik Olszewski'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 Dominik Olszewski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dominik Olszewski more than expected).
Fields of papers citing papers by Dominik Olszewski
This network shows the impact of papers produced by Dominik Olszewski. 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 Dominik Olszewski. The network helps show where Dominik Olszewski may publish in the future.
Co-authorship network of co-authors of Dominik Olszewski
This figure shows the co-authorship network connecting the top 25 collaborators of Dominik Olszewski. A scholar is included among the top collaborators of Dominik Olszewski 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 Dominik Olszewski. Dominik Olszewski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 3 | |
| 5 | 12 | |
| 6 | 8 | |
| 7 | 16 | |
| 8 | 4 | |
| 9 | 9 | |
| 10 | 95 | |
| 11 | 16 | |
| 12 | 5 | |
| 13 | Employing Probabilistic Dissimilarity for Feature Discovery in a Game of Chess | 0 |
| 14 | 52 | |
| 15 | A Probabilistic Component for K-Means Algorithm and its Application to Sound Recognition | 3 |
About Dominik Olszewski
Dominik Olszewski is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 15 papers that have together received 227 indexed citations. Recurring topics across this work include Face and Expression Recognition (6 papers), Advanced Clustering Algorithms Research (6 papers) and Neural Networks and Applications (4 papers). The work is most often cited by research in Artificial Intelligence (173 citations), Accounting (36 citations) and Information Systems (49 citations). Dominik Olszewski has collaborated with scholars based in Poland, Switzerland and Slovenia. Frequent co-authors include Marcin Iwanowski, Marcin Kołodziej, Gerardo Turcatti, Fabien Kuttler, Michael Bauer, Vardan Andriasyan, Fanny Georgi, Urs F. Greber and Maarit Suomalainen. Their work appears in journals such as Expert Systems with Applications, Pattern Recognition and Information Sciences.
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.