Tanja Harbaum

1.6k citations
33 papers · 69 · h-index 4

Impact in

Papers in

Journals
Computer Networks (1 paper)Procedia Computer Science (1 paper)Repository KITopen (Karlsruhe Institute of Technology) (4 papers)

In The Last Decade

Tanja Harbaum

24 papers receiving 69 citations

Peers

Tanja Harbaum
Comparison fields: 5 of 28
  • Hardware and Architecture 23
  • Software 4
  • Artificial Intelligence 26
  • Computer Networks and Communications 18
  • Computer Vision and Pattern Recognition 14
Replace Agustín Martínez with:
Agustín Martínez Spain
Jennifer Sheldon United States
Makai Mann United States
Greg Diamos China
Vesselin Velichkov Belgium
Klaus-Henning Noffz Germany
François Koeune Belgium
Bhavana Kanukurthi India
Mike Chrzanowski United States
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Tanja Harbaum relative to Agustín Martínez Spain Agustín Martínez's profile →
Citations per field
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Citations per year

Countries citing papers authored by Tanja Harbaum

Since Specialization
Citations

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

Fields of papers citing papers by Tanja Harbaum

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 19 scholars most cited alongside Tanja Harbaum, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tanja Harbaum Line = papers co-authored together Tanja Harbaum links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201916
2 201611
3 20234
4 20233
5 20233
6 20173
7 20223
8 20232
9 20222
10 20242
11 20232
12 20132
13 20232
14 20222
15 20182
16 20232
17 20231
18 20231
19 20241
20 20241

About Tanja Harbaum

Tanja Harbaum is a scholar working on Electrical and Electronic Engineering, Hardware and Architecture, Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications, having authored 33 papers that have together received 69 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (7 papers), Advanced Memory and Neural Computing (7 papers), Adversarial Robustness in Machine Learning (6 papers), Advanced Neural Network Applications (6 papers), Embedded Systems Design Techniques (6 papers), Interconnection Networks and Systems (5 papers), CCD and CMOS Imaging Sensors (4 papers) and Experimental Learning in Engineering (3 papers). The work is most often cited by research in Hardware and Architecture (23 citations), Software (4 citations), Artificial Intelligence (26 citations), Computer Networks and Communications (18 citations) and Computer Vision and Pattern Recognition (14 citations). Tanja Harbaum has collaborated with scholars based in Germany, United States and China. Frequent co-authors include Jürgen Becker, Klaus Hofmann, M. Weber, M. Balzer, Patrick Schmidt, Ludger Witte, Yu Wang, Benjamin Nuß, Jens Barth and Lars Bauer. Their work appears in journals such as Computer Networks, Procedia Computer Science and Repository KITopen (Karlsruhe Institute of Technology).

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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