Youngeun Kim

665 total citations
17 papers, 360 citations indexed

About

Youngeun Kim is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Youngeun Kim has authored 17 papers receiving a total of 360 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Electrical and Electronic Engineering, 9 papers in Artificial Intelligence and 5 papers in Cognitive Neuroscience. Recurrent topics in Youngeun Kim's work include Advanced Memory and Neural Computing (12 papers), Ferroelectric and Negative Capacitance Devices (6 papers) and Neural dynamics and brain function (5 papers). Youngeun Kim is often cited by papers focused on Advanced Memory and Neural Computing (12 papers), Ferroelectric and Negative Capacitance Devices (6 papers) and Neural dynamics and brain function (5 papers). Youngeun Kim collaborates with scholars based in United States, South Korea and United Arab Emirates. Youngeun Kim's co-authors include Priyadarshini Panda, Sungeun Hong, Donghyeon Cho, Di Wu, Dong‐Hyun Lee, Chenxi Wu, Hyunsoo Kim, Sang Joon Kim, Yuhang Li and George Em Karniadakis and has published in prestigious journals such as Neural Networks, IEEE Transactions on Circuits and Systems for Video Technology and SIAM Journal on Scientific Computing.

In The Last Decade

Youngeun Kim

12 papers receiving 354 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Youngeun Kim United States 8 211 169 119 94 29 17 360
Mathias Gehrig Switzerland 11 61 0.3× 143 0.8× 156 1.3× 42 0.4× 19 0.7× 15 355
Zhe Ma China 10 115 0.5× 88 0.5× 63 0.5× 56 0.6× 11 0.4× 29 284
Byunggook Na South Korea 6 141 0.7× 252 1.5× 75 0.6× 159 1.7× 46 1.6× 10 370
Federico Paredes-Vallés Netherlands 8 89 0.4× 201 1.2× 106 0.9× 83 0.9× 30 1.0× 15 326
Dingheng Wang China 9 116 0.5× 136 0.8× 88 0.7× 91 1.0× 17 0.6× 15 304
Anton Mitrokhin United States 7 125 0.6× 330 2.0× 162 1.4× 63 0.7× 29 1.0× 8 473
Aaron Chadha United Kingdom 7 84 0.4× 143 0.8× 121 1.0× 59 0.6× 17 0.6× 11 279
Xiao Ming-qing China 8 59 0.3× 104 0.6× 33 0.3× 101 1.1× 16 0.6× 25 209
Qingsong Liu China 8 41 0.2× 83 0.5× 45 0.4× 113 1.2× 31 1.1× 30 335

Countries citing papers authored by Youngeun Kim

Since Specialization
Citations

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

Fields of papers citing papers by Youngeun Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Youngeun Kim

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

All Works

17 of 17 papers shown
1.
Wu, Chenxi, et al.. (2025). Artificial to Spiking Neural Networks Conversion with Calibration in Scientific Machine Learning. SIAM Journal on Scientific Computing. 47(3). C559–C577.
2.
Kim, Youngeun, et al.. (2025). ReSpike: residual frames-based hybrid spiking neural networks for efficient action recognition. Neuromorphic Computing and Engineering. 5(1). 14009–14009.
3.
Li, Yuhang, et al.. (2025). Spiking Transformer with Spatial-Temporal Attention. 13948–13958.
4.
Kim, Youngeun, et al.. (2024). Workload-Balanced Pruning for Sparse Spiking Neural Networks. IEEE Transactions on Emerging Topics in Computational Intelligence. 8(4). 2897–2907. 13 indexed citations
5.
6.
Kim, Youngeun, et al.. (2024). TReX- Reusing Vision Transformer’s Attention for Efficient Xbar-Based Computing. IEEE Transactions on Emerging Topics in Computing. 13(3). 686–697.
7.
Kim, Youngeun, et al.. (2024). When in-memory computing meets spiking neural networks—A perspective on device-circuit-system-and-algorithm co-design. Applied Physics Reviews. 11(3). 2 indexed citations
8.
Kim, Youngeun, et al.. (2024). RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems. IEEE Transactions on Emerging Topics in Computational Intelligence. 8(2). 2101–2111. 1 indexed citations
9.
Kim, Youngeun, et al.. (2024). LoAS: Fully Temporal-Parallel Dataflow for Dual-Sparse Spiking Neural Networks. 1107–1121. 4 indexed citations
10.
Kim, Youngeun, et al.. (2023). Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing. arXiv (Cornell University). 619–624. 9 indexed citations
11.
Kim, Youngeun, et al.. (2022). Gradient-based Bit Encoding Optimization for Noise-Robust Binary Memristive Crossbar. 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). 1111–1114. 2 indexed citations
12.
Kim, Youngeun, et al.. (2022). Beyond classification: directly training spiking neural networks for semantic segmentation. Neuromorphic Computing and Engineering. 2(4). 44015–44015. 46 indexed citations
13.
Kim, Youngeun, et al.. (2022). Rate Coding Or Direct Coding: Which One Is Better For Accurate, Robust, And Energy-Efficient Spiking Neural Networks?. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 71–75. 48 indexed citations
14.
Kim, Youngeun & Sungeun Hong. (2021). Adaptive Graph Adversarial Networks for Partial Domain Adaptation. IEEE Transactions on Circuits and Systems for Video Technology. 32(1). 172–182. 27 indexed citations
15.
Kim, Youngeun & Priyadarshini Panda. (2021). Optimizing Deeper Spiking Neural Networks for Dynamic Vision Sensing. Neural Networks. 144. 686–698. 73 indexed citations
16.
Kim, Youngeun, et al.. (2021). Domain Adaptation Without Source Data. IEEE Transactions on Artificial Intelligence. 2(6). 508–518. 122 indexed citations
17.
Kim, Youngeun, Donghyeon Cho, & Sungeun Hong. (2020). Towards Privacy-Preserving Domain Adaptation. IEEE Signal Processing Letters. 27. 1675–1679. 13 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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