Tomasz Lehmann

452 citations
2 papers · 186 · 1 hit paper · h-index 1

Impact in

    • Artificial Intelligence in Healthcare and Education
    • Topic Modeling
    • Natural Language Processing Techniques
    • Semantic Web and Ontologies
    • Advanced Graph Neural Networks
    • Intelligent Tutoring Systems and Adaptive Learning
    • Reinforcement Learning in Robotics

Papers in

Journals
Annals of Computer Science and Information Systems (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
Partner nations
PolandSwitzerland

In The Last Decade

Tomasz Lehmann

1 paper receiving 181 citations

Tomasz Lehmann's Hit Papers

Graph of Thoughts: Solving Elaborate Problems with Large Language Models 2024 · 186 citations
1860+1Years since publication50100150

Peers

Tomasz Lehmann
Comparison fields: 5 of 51
  • Health Informatics 10
  • Artificial Intelligence 108
  • Software 9
  • Computer Vision and Pattern Recognition 27
  • Information Systems and Management 7
Replace Piotr Nyczyk with:
Piotr Nyczyk Switzerland
Ales Kubicek Switzerland
H. Niewiadomski Switzerland
Nils Blach Switzerland
Yeganeh Kordi United States
Samuel Weinbach United States
Dor Muhlgay Israel
Shijie Wang Hong Kong
Sidney Black United States
Viet-Man Le Austria
Tomasz Lehmann relative to Piotr Nyczyk Switzerland Piotr Nyczyk's profile →
Citations per field
00.5×1.5×
Piotr Nyczyk · 1×
Citations per year

Countries citing papers authored by Tomasz Lehmann

Since Specialization
Citations

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

Fields of papers citing papers by Tomasz Lehmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 10 scholars most cited alongside Tomasz Lehmann, 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 Tomasz Lehmann Line = papers co-authored together Tomasz Lehmann links everyone, so they are left out of the graph.

All Works

2 of 2 papers shown
#Work
1
Graph of Thoughts: Solving Elaborate Problems with Large Language Models
Hit paper breakdown →
2024186
2 20220

About Tomasz Lehmann

Tomasz Lehmann is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence, Infectious Diseases and Organic Chemistry, having authored 2 papers that have together received 186 indexed citations. Recurring topics across this work include Face recognition and analysis (1 paper), Natural Language Processing Techniques (1 paper), Topic Modeling (1 paper) and Biometric Identification and Security (1 paper). The work is most often cited by research in Health Informatics (10 citations), Artificial Intelligence (108 citations), Software (9 citations), Computer Vision and Pattern Recognition (27 citations) and Information Systems and Management (7 citations). Tomasz Lehmann has collaborated with scholars based in Poland and Switzerland. Frequent co-authors include Ales Kubicek, H. Niewiadomski, Nils Blach, Maciej Besta, Michał Podstawski, Lukas Gianinazzi, Robert Gerstenberger, Piotr Nyczyk, Torsten Hoefler and Andrzej Pacut. Their work appears in journals such as Annals of Computer Science and Information Systems and Proceedings of the AAAI Conference on Artificial Intelligence.

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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