Sven Giesselbach

949 total citations · 1 hit paper
10 papers, 544 citations indexed

About

Sven Giesselbach is a scholar working on Artificial Intelligence, Biomedical Engineering and Molecular Biology. According to data from OpenAlex, Sven Giesselbach has authored 10 papers receiving a total of 544 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 2 papers in Biomedical Engineering and 1 paper in Molecular Biology. Recurrent topics in Sven Giesselbach's work include Topic Modeling (3 papers), Gait Recognition and Analysis (2 papers) and Anomaly Detection Techniques and Applications (2 papers). Sven Giesselbach is often cited by papers focused on Topic Modeling (3 papers), Gait Recognition and Analysis (2 papers) and Anomaly Detection Techniques and Applications (2 papers). Sven Giesselbach collaborates with scholars based in Germany, Egypt and China. Sven Giesselbach's co-authors include Christian Bauckhage, Sebastian Mayer, Bogdan Georgiev, Raoul Heese, Katharina Beckh, Michał Walczak, Julius Pfrommer, Jochen Garcke, Rajkumar Ramamurthy and Birgit Kirsch and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, Computers in Biology and Medicine and IEEE Software.

In The Last Decade

Sven Giesselbach

9 papers receiving 523 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.

Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems

2021 469 citations Laura von Rueden, Sebastian Mayer et al. IEEE Transactions on Knowledge and Data Engineering profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sven Giesselbach Germany	5	201	70	57	55	53	10	544
Raoul Heese Germany	7	239 1.2×	94 1.3×	63 1.1×	57 1.0×	40 0.8×	20	589
Birgit Kirsch Germany	2	183 0.9×	68 1.0×	56 1.0×	50 0.9×	32 0.6×	3	519
Laura von Rueden Germany	3	187 0.9×	70 1.0×	57 1.0×	51 0.9×	31 0.6×	5	485
Katharina Beckh Germany	4	188 0.9×	70 1.0×	56 1.0×	51 0.9×	31 0.6×	8	493
Rajkumar Ramamurthy Germany	5	231 1.1×	68 1.0×	57 1.0×	50 0.9×	31 0.6×	13	537
Bogdan Georgiev Germany	2	186 0.9×	69 1.0×	58 1.0×	50 0.9×	31 0.6×	8	483
Jannis Schuecker Germany	5	189 0.9×	69 1.0×	80 1.4×	50 0.9×	32 0.6×	7	548
Yuchen Wu China	5	199 1.0×	46 0.7×	82 1.4×	86 1.6×	62 1.2×	12	664
Muhammad Ubale Kiru Malaysia	4	125 0.6×	43 0.6×	109 1.9×	36 0.7×	40 0.8×	5	478
Jianxiang Feng China	8	209 1.0×	79 1.1×	45 0.8×	32 0.6×	34 0.6×	27	630

Countries citing papers authored by Sven Giesselbach

Since Specialization

Citations

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

Fields of papers citing papers by Sven Giesselbach

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sven Giesselbach

This figure shows the co-authorship network connecting the top 25 collaborators of Sven Giesselbach. A scholar is included among the top collaborators of Sven Giesselbach 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 Sven Giesselbach. Sven Giesselbach 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:

10 of 10 papers shown

1.

Schäfer, Henning, Héctor Allende‐Cid, Giulia Baldini, et al.. (2025). From BERT to generative AI - Comparing encoder-only vs. large language models in a cohort of lung cancer patients for named entity recognition in unstructured medical reports. Computers in Biology and Medicine. 195. 110665–110665. 1 indexed citations

2.

Beckh, Katharina, Nilesh Chakraborty, Giulia Baldini, et al.. (2024). From admission to discharge: a systematic review of clinical natural language processing along the patient journey. BMC Medical Informatics and Decision Making. 24(1). 238–238. 6 indexed citations

3.

Giesselbach, Sven, et al.. (2024). Addressing a New Paradigm Shift in Data Science: An Empirical Study on Novel Project Characteristics for Foundation Model Projects. IEEE Software. 42(1). 84–92.

4.

Giesselbach, Sven, et al.. (2023). Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks. Frontiers in Bioengineering and Biotechnology. 11. 1204115–1204115. 3 indexed citations

5.

Paaß, Gerhard & Sven Giesselbach. (2023). Foundation Models for Natural Language Processing. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 17 indexed citations

6.

Kirsch, Birgit, et al.. (2022). Using Probabilistic Soft Logic to Improve Information Extraction in the Legal Domain. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 76–87. 1 indexed citations

7.

Rueden, Laura von, Sebastian Mayer, Katharina Beckh, et al.. (2021). Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems. IEEE Transactions on Knowledge and Data Engineering. 1–1. 469 indexed citations breakdown →

8.

Horst, Fabian, et al.. (2020). Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning. Frontiers in Bioengineering and Biotechnology. 8. 260–260. 41 indexed citations

9.

Horst, Fabian, et al.. (2019). Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Classification of Gait Using Machine Learning.. arXiv (Cornell University). 1 indexed citations

10.

Giesselbach, Sven, et al.. (2019). Improving Word Embeddings Using Kernel PCA. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 200–208. 5 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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