Thomas D. Nielsen
- Software top 2%
- Artificial Intelligence top 0.5%
- Bayesian Modeling and Causal Inference 42
- Bayesian Methods and Mixture Models 10
- AI-based Problem Solving and Planning 9
- Machine Learning and Algorithms 8
- Data Stream Mining Techniques 8
- Gaussian Processes and Bayesian Inference 7
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- Multi-Criteria Decision Making 6
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- Time Series Analysis and Forecasting 7
Thomas D. Nielsen
73 papers receiving 3.1k citations
Hit Papers
Peers
Comparison fields: 5 of 171
- Statistics, Probability and Uncertainty 539
- Software 249
- Artificial Intelligence 1.7k
- Management Science and Operations Research 437
- Radiological and Ultrasound Technology 150
Countries citing papers authored by Thomas D. Nielsen
This map shows the geographic impact of Thomas D. Nielsen'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 Thomas D. Nielsen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas D. Nielsen more than expected).
Fields of papers citing papers by Thomas D. Nielsen
This network shows the impact of papers produced by Thomas D. Nielsen. 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 Thomas D. Nielsen. The network helps show where Thomas D. Nielsen may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Thomas D. Nielsen, 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 | 2024 | 3 | |
| 2 | Metagenomic Binning using Connectivity-constrained Variational Autoencoders | 2023 | 1 |
| 3 | 2023 | 5 | |
| 4 | 2022 | 28 | |
| 5 | 2021 | 8 | |
| 6 | 2020 | 38 | |
| 7 | 2018 | 7 | |
| 8 | 2015 | 19 | |
| 9 | 2013 | 20 | |
| 10 | 2011 | 48 | |
| 11 | 2011 | 28 | |
| 12 | Towards a more expressive model for dynamic classification | 2010 | 3 |
| 13 | 2008 | 24 | |
| 14 | 2008 | 6 | |
| 15 | Adapting Bayes Network Structures to Non-stationary Domains | 2006 | 1 |
| 16 | 2005 | 25 | |
| 17 | 2004 | 22 | |
| 18 | 2004 | 25 | |
| 19 | Fusion of domain knowledge with data for structural learning in object oriented domains | 2003 | 31 |
| 20 | Structural Learning in Object Oriented Domains | 2001 | 11 |
About Thomas D. Nielsen
Thomas D. Nielsen is a scholar working on Artificial Intelligence, Software and General Decision Sciences, having authored 73 papers that have together received 3.4k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (42 papers), Bayesian Methods and Mixture Models (10 papers), AI-based Problem Solving and Planning (9 papers), Machine Learning and Algorithms (8 papers), Data Stream Mining Techniques (8 papers), Time Series Analysis and Forecasting (7 papers), Gaussian Processes and Bayesian Inference (7 papers) and Multi-Criteria Decision Making (6 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (539 citations), Software (249 citations) and Artificial Intelligence (1.7k citations). Thomas D. Nielsen has collaborated with scholars based in Denmark, Norway and Spain. Frequent co-authors include Finn V. Jensen, Helge Langseth, Antonio Salmerón, Rafael Rumí, Anders L. Madsen, Christian S. Jensen, Yingke Chen, Frank Jensen, Kim G. Larsen and Jean‐Yves Jaffray. Their work appears in journals such as Bioinformatics, European Journal of Operational Research and Pattern Recognition.
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.