Felix Laumann
Impact in
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- Sustainable Development and Environmental Policy
Papers in
-
- Gaussian Processes and Bayesian Inference 2
- Adversarial Robustness in Machine Learning 2
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- Energy, Environment, Economic Growth 1
- Complex Systems and Time Series Analysis 1
- Co-authors
- Mauricio Barahona (5 shared papers)Julius von Kügelgen (3 shared papers)Torben Tambo (2 shared papers)Kumar Shridhar (2 shared papers)Mette Møller Sørensen (1 shared paper)Anthony A Laverty (1 shared paper)David Sharp (1 shared paper)Marcus Liwicki (1 shared paper)
- Journals
- The Lancet Planetary Health (1 paper)Cell Reports (1 paper)Royal Society Open Science (1 paper)Sustainability (1 paper)Entropy (1 paper)
- Partner nations
- United KingdomGermanyDenmark
In The Last Decade
Felix Laumann
11 papers receiving 140 citations
Peers
Comparison fields: 5 of 67
- Nuclear Energy and Engineering 2
- Management, Monitoring, Policy and Law 25
- Strategy and Management 26
- Industrial and Manufacturing Engineering 11
- Environmental Engineering 15
Countries citing papers authored by Felix Laumann
This map shows the geographic impact of Felix Laumann'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 Felix Laumann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felix Laumann more than expected).
Fields of papers citing papers by Felix Laumann
This network shows the impact of papers produced by Felix Laumann. 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 Felix Laumann. The network helps show where Felix Laumann may publish in the future.
Co-authors
The 22 scholars most cited alongside Felix Laumann, 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 | 2022 | 65 | |
| 2 | 2018 | 23 | |
| 3 | 2017 | 13 | |
| 4 | Bayesian Convolutional Neural Networks | 2018 | 11 |
| 5 | 2021 | 10 | |
| 6 | Bayesian Convolutional Neural Networks with Variational Inference | 2018 | 7 |
| 7 | 2024 | 7 | |
| 8 | 2021 | 4 | |
| 9 | 2021 | 3 | |
| 10 | 2023 | 2 | |
| 11 | 2023 | 1 |
About Felix Laumann
Felix Laumann is a scholar working on Artificial Intelligence, Economics and Econometrics, Global and Planetary Change, Environmental Engineering and Electrical and Electronic Engineering, having authored 11 papers that have together received 146 indexed citations. Recurring topics across this work include Sustainability and Climate Change Governance (3 papers), Statistical Methods and Inference (2 papers), Sustainability and Ecological Systems Analysis (2 papers), Gaussian Processes and Bayesian Inference (2 papers), Adversarial Robustness in Machine Learning (2 papers), Energy, Environment, Economic Growth (1 paper), Sustainable Supply Chain Management (1 paper) and Complex Systems and Time Series Analysis (1 paper). The work is most often cited by research in Nuclear Energy and Engineering (2 citations), Management, Monitoring, Policy and Law (25 citations), Strategy and Management (26 citations), Industrial and Manufacturing Engineering (11 citations) and Environmental Engineering (15 citations). Felix Laumann has collaborated with scholars based in United Kingdom, Germany and Denmark. Frequent co-authors include Mauricio Barahona, Julius von Kügelgen, Torben Tambo, Kumar Shridhar, Mette Møller Sørensen, Anthony A Laverty, David Sharp, Marcus Liwicki, Neil Jennings and Paolo Vineis. Their work appears in journals such as The Lancet Planetary Health, Cell Reports, Royal Society Open Science, Sustainability and Entropy.
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.