Thomas D. Nielsen
- Artificial Intelligence top 0.5%
- Statistics, Probability and Uncertainty top 0.5%
- Management Science and Operations Research top 1%
- Information Systems top 2%
- Control and Systems Engineering top 5%
- Co-authors
- Finn V. JensenHelge LangsethAntonio SalmerónRafael RumíAnders L. MadsenChristian S. JensenYingke ChenFrank Jensen
- Topics
- Bayesian Modeling and Causal Inference (42 papers)Bayesian Methods and Mixture Models (10 papers)AI-based Problem Solving and Planning (9 papers)
In The Last Decade
Thomas D. Nielsen
73 papers receiving 3.1k citations
Hit Papers
Peers
Comparison fields: 5 of 171
- Artificial Intelligence 1.7k
- Statistics, Probability and Uncertainty 539
- Management Science and Operations Research 437
- Information Systems 353
- Control and Systems Engineering 304
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 of co-authors of Thomas D. Nielsen
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas D. Nielsen. A scholar is included among the top collaborators of Thomas D. Nielsen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Thomas D. Nielsen. Thomas D. Nielsen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | Metagenomic Binning using Connectivity-constrained Variational Autoencoders | 1 |
| 3 | 5 | |
| 4 | 28 | |
| 5 | 8 | |
| 6 | 38 | |
| 7 | 7 | |
| 8 | 19 | |
| 9 | 20 | |
| 10 | 48 | |
| 11 | 28 | |
| 12 | Towards a more expressive model for dynamic classification | 3 |
| 13 | 24 | |
| 14 | 6 | |
| 15 | Adapting Bayes Network Structures to Non-stationary Domains | 1 |
| 16 | 25 | |
| 17 | 22 | |
| 18 | 25 | |
| 19 | Fusion of domain knowledge with data for structural learning in object oriented domains | 31 |
| 20 | Structural Learning in Object Oriented Domains | 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) and AI-based Problem Solving and Planning (9 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.