Mateja Jamnik

1.5k citations
51 papers · 330 · h-index 11

Impact in

Papers in

    • Logic, programming, and type systems 10
    • Semantic Web and Ontologies 9
    • Logic, Reasoning, and Knowledge 7
    • Explainable Artificial Intelligence (XAI) 7
    • Adversarial Robustness in Machine Learning 4
    • AI-based Problem Solving and Planning 4
    • Data Visualization and Analytics 14

Mateja Jamnik

40 papers receiving 295 citations

Peers

Mateja Jamnik
Comparison fields: 5 of 88
  • Health Informatics 12
  • Theoretical Computer Science 6
  • Artificial Intelligence 165
  • Software 16
  • Computer Vision and Pattern Recognition 56
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Citations per field
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Citations per year

Countries citing papers authored by Mateja Jamnik

Since Specialization
Citations

This map shows the geographic impact of Mateja Jamnik'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 Mateja Jamnik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mateja Jamnik more than expected).

Fields of papers citing papers by Mateja Jamnik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mateja Jamnik. 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 Mateja Jamnik. The network helps show where Mateja Jamnik may publish in the future.

Co-authors

The 25 scholars most cited alongside Mateja Jamnik, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mateja Jamnik Line = papers co-authored together Mateja Jamnik links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 51 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201983
2 202424
3 202019
4 200517
5 199917
6 200116
7 200715
8
Mathematical reasoning with diagrams : from intuition to automation
200115
9 201715
10 202010
11 201510
12 20148
13 20237
14 20167
15
Automation of Diagrammatic Reasoning
19975
16
Automatic learning in proof planning
20024
17 20224
18 20244
19 20164
20 20034

About Mateja Jamnik

Mateja Jamnik is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Computational Theory and Mathematics and Software, having authored 51 papers that have together received 330 indexed citations. Recurring topics across this work include Data Visualization and Analytics (14 papers), Logic, programming, and type systems (10 papers), Semantic Web and Ontologies (9 papers), Logic, Reasoning, and Knowledge (7 papers), Explainable Artificial Intelligence (XAI) (7 papers), Advanced Database Systems and Queries (6 papers), Adversarial Robustness in Machine Learning (4 papers) and AI-based Problem Solving and Planning (4 papers). The work is most often cited by research in Health Informatics (12 citations), Theoretical Computer Science (6 citations), Artificial Intelligence (165 citations), Software (16 citations) and Computer Vision and Pattern Recognition (56 citations). Mateja Jamnik has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Píetro Lió, Nikola Simidjievski, Alan Bundy, Paul Scherer, Cristian Bodnar, Adrian Weller, Umang Bhatt, Manfred Kerber, Alan F. Blackwell and Ian Green. Their work appears in journals such as Journal of Logic Language and Information, Proceedings of the National Academy of Sciences, Bioinformatics, Journal of Applied Logic and Cambridge yearbook of European legal studies.

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.

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