Heike Leitte

1.0k total citations
38 papers, 539 citations indexed

About

Heike Leitte is a scholar working on Computer Vision and Pattern Recognition, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Heike Leitte has authored 38 papers receiving a total of 539 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Computer Vision and Pattern Recognition, 13 papers in Computational Theory and Mathematics and 10 papers in Artificial Intelligence. Recurrent topics in Heike Leitte's work include Data Visualization and Analytics (16 papers), Topological and Geometric Data Analysis (12 papers) and Advanced Vision and Imaging (6 papers). Heike Leitte is often cited by papers focused on Data Visualization and Analytics (16 papers), Topological and Geometric Data Analysis (12 papers) and Advanced Vision and Imaging (6 papers). Heike Leitte collaborates with scholars based in Germany, United States and United Kingdom. Heike Leitte's co-authors include Bastian Rieck, Christoph Garth, Jonas Lukasczyk, Alexander Schmitz, Ernst H. K. Stelzer, Daniel von Wangenheim, Richard S. Smith, Alexis Maizel, Ross Maciejewski and Leila De Floriani and has published in prestigious journals such as Current Biology, Journal of Chemical Information and Modeling and Computers & Chemical Engineering.

In The Last Decade

Heike Leitte

35 papers receiving 521 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Heike Leitte Germany 13 276 156 104 94 87 38 539
Elizabeth Munch United States 11 81 0.3× 343 2.2× 74 0.7× 14 0.1× 44 0.5× 35 487
Jerzy W. Jaromczyk United States 9 177 0.6× 97 0.6× 92 0.9× 42 0.4× 105 1.2× 42 705
Jarkko Venna Finland 12 422 1.5× 56 0.4× 187 1.8× 10 0.1× 364 4.2× 17 798
Július Parulek Norway 12 264 1.0× 35 0.2× 185 1.8× 7 0.1× 32 0.4× 26 467
E. Wes Bethel United States 15 208 0.8× 25 0.2× 51 0.5× 10 0.1× 80 0.9× 38 511
Keyan Ding China 7 655 2.4× 14 0.1× 36 0.3× 15 0.2× 78 0.9× 23 906
Ken Joy United States 12 266 1.0× 15 0.1× 38 0.4× 26 0.3× 58 0.7× 37 521
Dan Maljovec United States 7 219 0.8× 60 0.4× 21 0.2× 4 0.0× 117 1.3× 11 342
Nina S. T. Hirata Brazil 11 369 1.3× 34 0.2× 35 0.3× 5 0.1× 191 2.2× 67 586
Primož Škraba United States 15 169 0.6× 309 2.0× 43 0.4× 2 0.0× 47 0.5× 34 562

Countries citing papers authored by Heike Leitte

Since Specialization
Citations

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

Fields of papers citing papers by Heike Leitte

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Heike Leitte

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

All Works

20 of 20 papers shown
1.
Leitte, Heike, et al.. (2025). Hierarchical matrix completion for the prediction of properties of binary mixtures. Computers & Chemical Engineering. 199. 109122–109122. 2 indexed citations
2.
Jirasek, Fabian, et al.. (2025). Using large language models for solving textbook-style thermodynamic problems. Computers & Chemical Engineering. 204. 109333–109333.
3.
Leitte, Heike, et al.. (2024). Informed Machine Learning for Optimizing Melt Spinning Processes. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 706–713.
4.
Jirasek, Fabian, et al.. (2024). KnowTD─An Actionable Knowledge Representation System for Thermodynamics. Journal of Chemical Information and Modeling. 64(15). 5878–5887. 1 indexed citations
5.
Jirasek, Fabian, Maja Rudolph, Daniel Neider, et al.. (2023). Deep Anomaly Detection on Tennessee Eastman Process Data. Chemie Ingenieur Technik. 95(7). 1077–1082. 7 indexed citations
6.
Jirasek, Fabian, et al.. (2021). Attribute-based Explanation of Non-Linear Embeddings of High-Dimensional Data. IEEE Transactions on Visualization and Computer Graphics. 28(1). 540–550. 17 indexed citations
7.
Hamann, Bernd, et al.. (2021). Decomposing deviations of scanned surfaces of sheet metal assemblies. Journal of Manufacturing Systems. 61. 125–138. 4 indexed citations
8.
Hagen, Hans, et al.. (2020). ConceptGraph: A Formal Model for Interpretation and Reasoning During Visual Analysis. Computer Graphics Forum. 39(6). 5–18. 7 indexed citations
9.
Hamann, Bernd, et al.. (2020). Combining Visual Analytics and Machine Learning for Reverse Engineering in Assembly Quality Control. Journal of Imaging Science and Technology. 64(6). 60405–1. 2 indexed citations
10.
Antón, Simon D. Duque, et al.. (2019). Security in Process: Visually Supported Triage Analysis in Industrial Process Data. arXiv (Cornell University). 14 indexed citations
11.
Lukasczyk, Jonas, Jeffrey D. Hyman, G. Srinivasan, et al.. (2019). A Query-Based Framework for Searching, Sorting, and Exploring Data Ensembles. Computing in Science & Engineering. 22(2). 64–76. 1 indexed citations
12.
Böttinger, Michael, et al.. (2018). Visual exploration of ensemble variability at the example of decadal climate predictions. EGU General Assembly Conference Abstracts. 10206. 1 indexed citations
13.
Lukasczyk, Jonas, et al.. (2018). VOIDGA: A View-Approximation Oriented Image Database Generation Approach. 12–22. 2 indexed citations
14.
Rieck, Bastian, et al.. (2017). Clique Community Persistence: A Topological Visual Analysis Approach for Complex Networks. IEEE Transactions on Visualization and Computer Graphics. 24(1). 822–831. 31 indexed citations
15.
Lukasczyk, Jonas, Gunther H. Weber, Ross Maciejewski, Christoph Garth, & Heike Leitte. (2017). Nested Tracking Graphs. Computer Graphics Forum. 36(3). 12–22. 33 indexed citations
16.
Wangenheim, Daniel von, Alexander Schmitz, Richard S. Smith, et al.. (2016). Rules and Self-Organizing Properties of Post-embryonic Plant Organ Cell Division Patterns. Current Biology. 26(4). 439–449. 111 indexed citations
17.
Heine, Christian, Heike Leitte, Mark W. Hlawitschka, et al.. (2016). A Survey of Topology‐based Methods in Visualization. Computer Graphics Forum. 35(3). 643–667. 85 indexed citations
18.
Rieck, Bastian & Heike Leitte. (2016). Exploring and Comparing Clusterings of Multivariate Data Sets Using Persistent Homology. Computer Graphics Forum. 35(3). 81–90. 13 indexed citations
19.
Rieck, Bastian & Heike Leitte. (2015). Persistent Homology for the Evaluation of Dimensionality Reduction Schemes. Computer Graphics Forum. 34(3). 431–440. 34 indexed citations
20.
Rieck, Bastian, Hubert Mara, & Heike Leitte. (2012). Multivariate Data Analysis Using Persistence-Based Filtering and Topological Signatures. IEEE Transactions on Visualization and Computer Graphics. 18(12). 2382–2391. 28 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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