Thomas D. Nielsen

6.0k total citations · 1 hit paper
73 papers, 3.4k citations indexed

About

Thomas D. Nielsen is a scholar working on Artificial Intelligence, Management Science and Operations Research and Signal Processing. According to data from OpenAlex, Thomas D. Nielsen has authored 73 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Artificial Intelligence, 13 papers in Management Science and Operations Research and 11 papers in Signal Processing. Recurrent topics in Thomas D. Nielsen's 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). Thomas D. Nielsen is often cited by papers focused on Bayesian Modeling and Causal Inference (42 papers), Bayesian Methods and Mixture Models (10 papers) and AI-based Problem Solving and Planning (9 papers). Thomas D. Nielsen collaborates with scholars based in Denmark, Norway and Spain. Thomas D. Nielsen's 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 and has published in prestigious journals such as Bioinformatics, European Journal of Operational Research and Pattern Recognition.

In The Last Decade

Thomas D. Nielsen

73 papers receiving 3.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Bayesian Networks and Decision Graphs

2007 2.4k citations Finn V. Jensen, Thomas D. Nielsen profile →

Peers

Thomas D. Nielsen

AI

SPU

MSOR

IS

CSE

Ann E. Nicholson Australia

Kevin B. Korb Australia

David M. Chickering United States

Timothy J. Ross United States

Chelsea C. White United States

Agnar Aamodt Norway

Martin Neil United Kingdom

Ashok N. Srivastava United States

Ferat Sahin United States

Xinyang Deng China

Ann E. Nicholson Australia

Thomas D. Nielsen

1.4k ×0.8

AI

188 ×0.3

SPU

298 ×0.7

MSOR

257 ×0.7

IS

177 ×0.6

CSE

Citations per year, relative to Thomas D. Nielsen Thomas D. Nielsen (= 1×) peers Ann E. Nicholson

Countries citing papers authored by Thomas D. Nielsen

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Nielsen, Thomas D., et al.. (2024). A study on the risk stratification for patients within 24 hours of admission for risk of hospital-acquired urinary tract infection using Bayesian network models. Health Informatics Journal. 30(1). 1217719608–1217719608. 3 indexed citations

2.

Lamúrias, André, Alessandro Tibo, Katja Hose, Thomas D. Nielsen, & Mads Albertsen. (2023). Metagenomic Binning using Connectivity-constrained Variational Autoencoders. VBN Forskningsportal (Aalborg Universitet). 1 indexed citations

3.

Nielsen, Thomas D., et al.. (2023). Clinically explainable machine learning models for early identification of patients at risk of hospital-acquired urinary tract infection. Journal of Hospital Infection. 154. 112–121. 5 indexed citations

4.

Lamúrias, André, Mantas Sereika, Mads Albertsen, Katja Hose, & Thomas D. Nielsen. (2022). Metagenomic binning with assembly graph embeddings. Bioinformatics. 38(19). 4481–4487. 28 indexed citations

5.

Langseth, Helge, et al.. (2021). Probabilistic Models with Deep Neural Networks. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 8 indexed citations

6.

Jensen, Christian S., et al.. (2020). Relational Fusion Networks: Graph Convolutional Networks for Road Networks. IEEE Transactions on Intelligent Transportation Systems. 23(1). 418–429. 38 indexed citations

7.

Masegosa, Andrés R., Antonio Salmerón, Rafael Rumí, et al.. (2018). Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. International Journal of Approximate Reasoning. 100. 115–134. 7 indexed citations

8.

Gámez, José A., et al.. (2015). A scalable pairwise class interaction framework for multidimensional classification. International Journal of Approximate Reasoning. 68. 194–210. 19 indexed citations

9.

Langseth, Helge, et al.. (2013). Learning mixtures of truncated basis functions from data. International Journal of Approximate Reasoning. 55(4). 940–956. 20 indexed citations

10.

Langseth, Helge, Thomas D. Nielsen, Rafael Rumí, & Antonio Salmerón. (2011). Mixtures of truncated basis functions. International Journal of Approximate Reasoning. 53(2). 212–227. 48 indexed citations

11.

Langseth, Helge & Thomas D. Nielsen. (2011). A latent model for collaborative filtering. International Journal of Approximate Reasoning. 53(4). 447–466. 28 indexed citations

12.

Martínez, Ana María, et al.. (2010). Towards a more expressive model for dynamic classification. VBN Forskningsportal (Aalborg Universitet). 563–564. 3 indexed citations

13.

Nielsen, Thomas D., et al.. (2008). Adapting Bayes network structures to non-stationary domains. International Journal of Approximate Reasoning. 49(2). 379–397. 24 indexed citations

14.

Jensen, Finn V., et al.. (2008). A comparison of two approaches for solving unconstrained influence diagrams. International Journal of Approximate Reasoning. 50(1). 153–173. 6 indexed citations

15.

Nielsen, Thomas D., et al.. (2006). Adapting Bayes Network Structures to Non-stationary Domains. VBN Forskningsportal (Aalborg Universitet). 223–230. 1 indexed citations

16.

Jensen, Finn V., Thomas D. Nielsen, & Prakash P. Shenoy. (2005). Sequential influence diagrams: A unified asymmetry framework. International Journal of Approximate Reasoning. 42(1-2). 101–118. 25 indexed citations

17.

Nielsen, Thomas D. & Finn V. Jensen. (2004). Learning a decision maker's utility function from (possibly) inconsistent behavior. Artificial Intelligence. 160(1-2). 53–78. 22 indexed citations

18.

Zhang, Nevin L., Thomas D. Nielsen, & Finn V. Jensen. (2004). Latent variable discovery in classification models. Artificial Intelligence in Medicine. 30(3). 283–299. 25 indexed citations

19.

Langseth, Helge & Thomas D. Nielsen. (2003). Fusion of domain knowledge with data for structural learning in object oriented domains. Journal of Machine Learning Research. 4(3). 339–368. 31 indexed citations

20.

Langseth, Helge, et al.. (2001). Structural Learning in Object Oriented Domains. VBN Forskningsportal (Aalborg Universitet). 340–344. 11 indexed citations

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.

Explore authors with similar magnitude of impact