Niels Landwehr

3.1k total citations · 1 hit paper
45 papers, 1.9k citations indexed

About

Niels Landwehr is a scholar working on Artificial Intelligence, Information Systems and Signal Processing. According to data from OpenAlex, Niels Landwehr has authored 45 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 11 papers in Information Systems and 6 papers in Signal Processing. Recurrent topics in Niels Landwehr's work include Data Mining Algorithms and Applications (10 papers), Machine Learning and Algorithms (9 papers) and Bayesian Modeling and Causal Inference (8 papers). Niels Landwehr is often cited by papers focused on Data Mining Algorithms and Applications (10 papers), Machine Learning and Algorithms (9 papers) and Bayesian Modeling and Causal Inference (8 papers). Niels Landwehr collaborates with scholars based in Germany, Belgium and United States. Niels Landwehr's co-authors include Eibe Frank, Mark Hall, Luc De Raedt, Tobias Scheffer, Nicolas Kuehn, Norman Abrahamson, Paolo Frasconi, Kristian Kersting, Hanna Drimalla and Isabel Dziobek and has published in prestigious journals such as Kidney International, Frontiers in Plant Science and BMC Bioinformatics.

In The Last Decade

Niels Landwehr

43 papers receiving 1.8k citations

Hit Papers

align trajectories

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Niels Landwehr Germany	19	651	235	201	164	153	45	1.9k
Mariette Awad Lebanon	20	702 1.1×	194 0.8×	112 0.6×	87 0.5×	39 0.3×	107	3.1k
Tareq Abed Mohammed Iraq	7	754 1.2×	175 0.7×	86 0.4×	147 0.9×	97 0.6×	18	2.7k
Tzu-Tsung Wong Taiwan	12	593 0.9×	145 0.6×	130 0.6×	73 0.4×	207 1.4×	19	2.2k
Manuel Fernández-Delgado Spain	21	915 1.4×	242 1.0×	99 0.5×	163 1.0×	222 1.5×	86	3.3k
Eva Cernadas Spain	16	832 1.3×	181 0.8×	80 0.4×	163 1.0×	227 1.5×	59	2.7k
Asdrúbal López‐Chau Mexico	12	639 1.0×	176 0.7×	102 0.5×	125 0.8×	113 0.7×	61	2.1k
Lazaros Iliadis Greece	23	497 0.8×	179 0.8×	152 0.8×	48 0.3×	50 0.3×	128	1.6k
Nachaat Mohamed India	14	654 1.0×	181 0.8×	136 0.7×	61 0.4×	67 0.4×	49	2.4k
Hui Chen China	26	506 0.8×	266 1.1×	54 0.3×	86 0.5×	119 0.8×	226	2.2k
Jair Cervantes Mexico	15	688 1.1×	200 0.9×	98 0.5×	123 0.8×	126 0.8×	48	2.3k

Countries citing papers authored by Niels Landwehr

Since Specialization

Citations

This map shows the geographic impact of Niels Landwehr'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 Niels Landwehr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Niels Landwehr more than expected).

Fields of papers citing papers by Niels Landwehr

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Niels Landwehr. 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 Niels Landwehr. The network helps show where Niels Landwehr may publish in the future.

Co-authorship network of co-authors of Niels Landwehr

This figure shows the co-authorship network connecting the top 25 collaborators of Niels Landwehr. A scholar is included among the top collaborators of Niels Landwehr 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 Niels Landwehr. Niels Landwehr is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Landwehr, Niels, et al.. (2022). Deep Learning Object Detection for Image Analysis of Cherry Fruit Fly (Rhagoletis cerasi L.) on Yellow Sticky Traps. Gesunde Pflanzen. 75(1). 37–48. 10 indexed citations

2.

Zhang, Guoqiang, et al.. (2021). Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building. Computers and Electronics in Agriculture. 187. 106259–106259. 9 indexed citations

3.

Hempel, Sabrina, et al.. (2020). Supervised Machine Learning to Assess Methane Emissions of a Dairy Building with Natural Ventilation. Applied Sciences. 10(19). 6938–6938. 19 indexed citations

4.

Hempel, Sabrina, Julian Adolphs, Niels Landwehr, David Janke, & Thomas Amon. (2020). How the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn—Classical Statistics versus Machine Learning. Sustainability. 12(3). 1030–1030. 15 indexed citations

5.

Drimalla, Hanna, et al.. (2020). Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT). npj Digital Medicine. 3(1). 25–25. 38 indexed citations

6.

Kuehn, Nicolas, Sreeram Reddy Kotha, & Niels Landwehr. (2019). A Non-ergodic GMPE for Europe and the Middle East with Spatially Varying Coefficients. Publication Database GFZ (GFZ German Research Centre for Geosciences). 11166. 2 indexed citations

7.

Theuerl, Susanne, Christiane Herrmann, Monika Heiermann, et al.. (2019). The Future Agricultural Biogas Plant in Germany: A Vision. Energies. 12(3). 396–396. 141 indexed citations

8.

Frasconi, Paolo, Niels Landwehr, Giuseppe Manco, & Jilles Vreeken. (2016). Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2016). Springer eBooks. 15 indexed citations

9.

Prasse, Paul, et al.. (2015). Learning to identify concise regular expressions that describe email campaigns. Journal of Machine Learning Research. 16(1). 3687–3720. 9 indexed citations

10.

Prasse, Paul, et al.. (2012). Learning to Identify Regular Expressions that Describe Email Campaigns. International Conference on Machine Learning. 1115–1122. 5 indexed citations

11.

Landwehr, Niels, et al.. (2012). Active Comparison of Prediction Models. Neural Information Processing Systems. 25. 1754–1762. 2 indexed citations

12.

Landwehr, Niels, et al.. (2010). Active Estimation of F-Measures. Neural Information Processing Systems. 23. 2083–2091. 11 indexed citations

13.

Thon, Ingo, et al.. (2009). Probabilistic logical sequence learning for video. Lirias (KU Leuven). 5 indexed citations

14.

Thon, Ingo, Bernd Gutmann, Martijn van Otterlo, Niels Landwehr, & Luc De Raedt. (2009). From non-deterministic to probabilistic planning with the help of statistical relational learning. Neuroscience Research. 28(3). 23–30. 4 indexed citations

15.

Landwehr, Niels. (2008). Modeling interleaved hidden processes. Lirias (KU Leuven). 520–527. 18 indexed citations

16.

Raedt, Luc De, Bart Demoen, Daan Fierens, et al.. (2008). Towards digesting the alphabet-soup of statistical relational learning. Lirias (KU Leuven). 17 indexed citations

17.

Landwehr, Niels & Luc De Raedt. (2007). r-grams: relational grams. Lirias. 907–912. 3 indexed citations

18.

Landwehr, Niels, Kristian Kersting, & Luc De Raedt. (2007). Integrating Naïve Bayes and FOIL. Journal of Machine Learning Research. 8(18). 481–507. 36 indexed citations

19.

Landwehr, Niels, Andrea Passerini, Luc De Raedt, & Paolo Frasconi. (2006). kFOIL: learning simple relational kernels. Lirias (KU Leuven). 389–394. 46 indexed citations

20.

Landwehr, Niels, et al.. (2006). Constrained hidden Markov models for population-based haplotyping (Extended Abstract). BMC Bioinformatics. 8. 1–6. 1 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.

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