Tanja Harbaum

1.6k total citations
33 papers, 69 citations indexed

About

Tanja Harbaum is a scholar working on Electrical and Electronic Engineering, Hardware and Architecture and Computer Vision and Pattern Recognition. According to data from OpenAlex, Tanja Harbaum has authored 33 papers receiving a total of 69 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Electrical and Electronic Engineering, 10 papers in Hardware and Architecture and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Tanja Harbaum's work include Advanced Memory and Neural Computing (7 papers), Parallel Computing and Optimization Techniques (7 papers) and Adversarial Robustness in Machine Learning (6 papers). Tanja Harbaum is often cited by papers focused on Advanced Memory and Neural Computing (7 papers), Parallel Computing and Optimization Techniques (7 papers) and Adversarial Robustness in Machine Learning (6 papers). Tanja Harbaum collaborates with scholars based in Germany, United States and China. Tanja Harbaum's co-authors include Jürgen Becker, Klaus Hofmann, M. Balzer, M. Weber, Patrick Schmidt, Andreas Barth, Lars Bauer, Zhenhua Zhu, Yu Wang and Benjamin Nuß and has published in prestigious journals such as Computer Networks, Repository KITopen (Karlsruhe Institute of Technology) and Procedia Computer Science.

In The Last Decade

Tanja Harbaum

24 papers receiving 69 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tanja Harbaum Germany 4 26 23 21 18 14 33 69
Jennifer Sheldon United States 5 49 1.9× 23 1.0× 24 1.1× 29 1.6× 6 0.4× 7 81
Gabriele Oliaro United States 5 20 0.8× 10 0.4× 15 0.7× 30 1.7× 9 0.6× 13 77
Makai Mann United States 5 13 0.5× 24 1.0× 11 0.5× 14 0.8× 7 0.5× 10 51
Agustín Martínez Spain 4 17 0.7× 22 1.0× 9 0.4× 12 0.7× 5 0.4× 19 50
Klaus-Henning Noffz Germany 7 25 1.0× 21 0.9× 16 0.8× 26 1.4× 29 2.1× 16 74
Greg Diamos China 3 50 1.9× 22 1.0× 24 1.1× 30 1.7× 42 3.0× 5 101
François Koeune Belgium 5 40 1.5× 11 0.5× 16 0.8× 28 1.6× 18 1.3× 9 68
Alfredo Petrosino Italy 6 14 0.5× 9 0.4× 5 0.2× 10 0.6× 33 2.4× 14 64
Itai Dinur Israel 6 49 1.9× 10 0.4× 6 0.3× 10 0.6× 29 2.1× 17 63
Bhavana Kanukurthi India 2 26 1.0× 9 0.4× 27 1.3× 26 1.4× 23 1.6× 6 65

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-authorship network of co-authors of Tanja Harbaum

This figure shows the co-authorship network connecting the top 25 collaborators of Tanja Harbaum. A scholar is included among the top collaborators of Tanja Harbaum 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 Tanja Harbaum. Tanja Harbaum 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
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Harbaum, Tanja, et al.. (2024). On Metric-Driven Development of Embedded Neuromorphic AI. 1–6. 1 indexed citations
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Harbaum, Tanja, et al.. (2023). Non-Intrusive Runtime Monitoring for Manycore Prototypes. 31–38.
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Harbaum, Tanja, et al.. (2023). An Analytical Model of Configurable Systolic Arrays to find the Best-Fitting Accelerator for a given DNN Workload. Repository KITopen (Karlsruhe Institute of Technology). 73–78. 2 indexed citations
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Harbaum, Tanja, et al.. (2023). A Hardware-Centric Approach to Increase and Prune Regular Activation Sparsity in CNNs. 1–5. 1 indexed citations
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Schmidt, Patrick, et al.. (2023). CNNParted: An open source framework for efficient Convolutional Neural Network inference partitioning in embedded systems. Computer Networks. 229. 109759–109759. 4 indexed citations
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Harbaum, Tanja, et al.. (2023). DREAM: Distributed Reinforcement Learning Enabled Adaptive Mixed-Critical NoC. 9. 1–6. 3 indexed citations
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Harbaum, Tanja, et al.. (2023). Reinforcement Learning Enabled Multi-Layered NoC for Mixed Criticality Systems. 38–44. 1 indexed citations
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Harbaum, Tanja, et al.. (2022). Hardware-aware Workload Distribution for AI-based Online Handwriting Recognition in a Sensor Pen. Repository KITopen (Karlsruhe Institute of Technology). 1–4. 2 indexed citations
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Harbaum, Tanja, et al.. (2019). A Hardware Perspective on the ChaCha Ciphers: Scalable Chacha8/12/20 Implementations Ranging from 476 Slices to Bitrates of 175 Gbit/s. Repository KITopen (Karlsruhe Institute of Technology). 294–299. 16 indexed citations
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Harbaum, Tanja, et al.. (2017). Auto-SI: An adaptive reconfigurable processor with run-time loop detection and acceleration. 153–158. 3 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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