Nils Blach
Impact in
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- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Semantic Web and Ontologies
- Advanced Graph Neural Networks
- Intelligent Tutoring Systems and Adaptive Learning
Papers in
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- Advanced Graph Neural Networks 2
- Topic Modeling 2
- Natural Language Processing Techniques 1
- Semantic Web and Ontologies 1
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- Interconnection Networks and Systems 1
- Co-authors
- Maciej Besta (4 shared papers)Torsten Hoefler (4 shared papers)Michał Podstawski (2 shared papers)Robert Gerstenberger (3 shared papers)H. Niewiadomski (3 shared papers)Ales Kubicek (2 shared papers)Piotr Nyczyk (2 shared papers)Tomasz Lehmann (1 shared paper)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- SwitzerlandGermanyUnited States
In The Last Decade
Nils Blach
4 papers receiving 208 citations
Nils Blach's Hit Papers
Peers
Comparison fields: 5 of 52
- Health Informatics 10
- Artificial Intelligence 123
- Software 9
- Computer Vision and Pattern Recognition 36
- Information Systems and Management 9
Countries citing papers authored by Nils Blach
This map shows the geographic impact of Nils Blach'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 Nils Blach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nils Blach more than expected).
Fields of papers citing papers by Nils Blach
This network shows the impact of papers produced by Nils Blach. 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 Nils Blach. The network helps show where Nils Blach may publish in the future.
Co-authors
The 20 scholars most cited alongside Nils Blach, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Graph of Thoughts: Solving Elaborate Problems with Large Language Models Hit paper breakdown → | 2024 | 200 |
| 2 | 2023 | 7 | |
| 3 | 2024 | 5 | |
| 4 | 2025 | 1 |
About Nils Blach
Nils Blach is a scholar working on Artificial Intelligence, Computer Networks and Communications, Information Systems, Computer Vision and Pattern Recognition and Hardware and Architecture, having authored 4 papers that have together received 213 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (2 papers), Topic Modeling (2 papers), Cloud Computing and Resource Management (1 paper), Natural Language Processing Techniques (1 paper), Embedded Systems Design Techniques (1 paper), Parallel Computing and Optimization Techniques (1 paper), Interconnection Networks and Systems (1 paper) and Semantic Web and Ontologies (1 paper). The work is most often cited by research in Health Informatics (10 citations), Artificial Intelligence (123 citations), Software (9 citations), Computer Vision and Pattern Recognition (36 citations) and Information Systems and Management (9 citations). Nils Blach has collaborated with scholars based in Switzerland, Germany and United States. Frequent co-authors include Maciej Besta, Torsten Hoefler, Michał Podstawski, Robert Gerstenberger, H. Niewiadomski, Ales Kubicek, Piotr Nyczyk, Tomasz Lehmann, Lukas Gianinazzi and Marek T. Michalewicz. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence 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.