Jonas Lederer
Impact in
- Artificial Intelligence top 10%
- Explainable Artificial Intelligence (XAI)
- Advanced Graph Neural Networks
- Adversarial Robustness in Machine Learning
- Topic Modeling
- Anomaly Detection Techniques and Applications
- Machine Learning in Healthcare
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- Computational Drug Discovery Methods
Papers in
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- Machine Learning in Materials Science 6
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- Explainable Artificial Intelligence (XAI) 3
- Topic Modeling 2
- Machine Learning and Data Classification 1
- Adversarial Robustness in Machine Learning 1
- Co-authors
- Kristof Sch utt (3 shared papers)Klaus- Robert Müller (5 shared papers)Grégoire Montavon (4 shared papers)Thomas Schnake (3 shared papers)Shinichi Nakajima (2 shared papers)Oliver Eberle (1 shared paper)Michael Gastegger (2 shared papers)Niklas W. A. Gebauer (1 shared paper)
- Journals
- Advanced Theory and Simulations (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)The Journal of Chemical Physics (1 paper)Journal of Chemical Theory and Computation (1 paper)IEEE Signal Processing Magazine (1 paper)
- Partner nations
- GermanySouth KoreaUnited States
In The Last Decade
Jonas Lederer
8 papers receiving 395 citations
Peers
Comparison fields: 5 of 73
- Artificial Intelligence 182
- Computational Theory and Mathematics 63
- Materials Chemistry 145
- Information Systems and Management 15
- Health Informatics 2
Countries citing papers authored by Jonas Lederer
This map shows the geographic impact of Jonas Lederer'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 Jonas Lederer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonas Lederer more than expected).
Fields of papers citing papers by Jonas Lederer
This network shows the impact of papers produced by Jonas Lederer. 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 Jonas Lederer. The network helps show where Jonas Lederer may publish in the future.
Co-authors
The 20 scholars most cited alongside Jonas Lederer, 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 | 2021 | 185 | |
| 2 | 2022 | 75 | |
| 3 | 2023 | 68 | |
| 4 | 2018 | 48 | |
| 5 | 2023 | 9 | |
| 6 | 2025 | 9 | |
| 7 | 2025 | 6 | |
| 8 | 2025 | 3 |
About Jonas Lederer
Jonas Lederer is a scholar working on Materials Chemistry, Artificial Intelligence, Computational Theory and Mathematics, Information Systems and Management and Spectroscopy, having authored 8 papers that have together received 403 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (6 papers), Explainable Artificial Intelligence (XAI) (3 papers), Computational Drug Discovery Methods (2 papers), Topic Modeling (2 papers), Machine Learning and Data Classification (1 paper), Scientific Computing and Data Management (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Metabolomics and Mass Spectrometry Studies (1 paper). The work is most often cited by research in Artificial Intelligence (182 citations), Computational Theory and Mathematics (63 citations), Materials Chemistry (145 citations), Information Systems and Management (15 citations) and Health Informatics (2 citations). Jonas Lederer has collaborated with scholars based in Germany, South Korea and United States. Frequent co-authors include Kristof Sch utt, Klaus- Robert Müller, Grégoire Montavon, Thomas Schnake, Shinichi Nakajima, Oliver Eberle, Michael Gastegger, Niklas W. A. Gebauer, Simon Letzgus and Patrick L Wagner. Their work appears in journals such as Advanced Theory and Simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence, The Journal of Chemical Physics, Journal of Chemical Theory and Computation and IEEE Signal Processing Magazine.
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.