Jong Hwan Ko

1.4k total citations · 1 hit paper
88 papers, 702 citations indexed

About

Jong Hwan Ko is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Jong Hwan Ko has authored 88 papers receiving a total of 702 indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Computer Vision and Pattern Recognition, 37 papers in Electrical and Electronic Engineering and 21 papers in Artificial Intelligence. Recurrent topics in Jong Hwan Ko's work include Advanced Memory and Neural Computing (24 papers), Advanced Neural Network Applications (16 papers) and Ferroelectric and Negative Capacitance Devices (14 papers). Jong Hwan Ko is often cited by papers focused on Advanced Memory and Neural Computing (24 papers), Advanced Neural Network Applications (16 papers) and Ferroelectric and Negative Capacitance Devices (14 papers). Jong Hwan Ko collaborates with scholars based in South Korea, United States and Singapore. Jong Hwan Ko's co-authors include Saibal Mukhopadhyay, Taesik Na, Burhan Ahmad Mudassar, Joo Chan Lee, Eunbyung Park, Duck-Hwan Kim, Monodeep Kar, Arvind Singh, Jaeha Kung and Sungmin Moon and has published in prestigious journals such as Advanced Materials, SHILAP Revista de lepidopterología and ACS Nano.

In The Last Decade

Jong Hwan Ko

76 papers receiving 683 citations

Hit Papers

Compact 3D Gaussian Representation for Radiance Field 2024 2026 2025 2024 20 40 60
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.

  1. Compact 3D Gaussian Representation for Radiance Field
    2024 63 citations Joo Chan Lee, Jong Hwan Ko et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jong Hwan Ko South Korea 16 300 290 156 76 67 88 702
Qi Xu China 14 203 0.7× 308 1.1× 147 0.9× 80 1.1× 78 1.2× 62 737
Ching‐Te Chiu Taiwan 14 391 1.3× 175 0.6× 110 0.7× 131 1.7× 78 1.2× 66 732
Eunhyeok Park South Korea 11 486 1.6× 210 0.7× 235 1.5× 92 1.2× 109 1.6× 26 715
Yuesheng Zhu China 17 738 2.5× 72 0.2× 221 1.4× 153 2.0× 33 0.5× 152 1.1k
Wen‐Jyi Hwang Taiwan 14 278 0.9× 141 0.5× 135 0.9× 42 0.6× 33 0.5× 82 686
Zhen Dong China 17 732 2.4× 366 1.3× 408 2.6× 35 0.5× 38 0.6× 43 1.2k
Saeid Nooshabadi United States 17 298 1.0× 516 1.8× 139 0.9× 248 3.3× 73 1.1× 164 986
Jinshan Yue China 15 325 1.1× 689 2.4× 187 1.2× 81 1.1× 199 3.0× 73 905
Yu Hu China 16 261 0.9× 515 1.8× 128 0.8× 61 0.8× 529 7.9× 109 944
Xuefei Ning China 13 184 0.6× 420 1.4× 281 1.8× 62 0.8× 123 1.8× 44 737

Countries citing papers authored by Jong Hwan Ko

Since Specialization
Citations

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

Fields of papers citing papers by Jong Hwan Ko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jong Hwan Ko. 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 Jong Hwan Ko. The network helps show where Jong Hwan Ko may publish in the future.

Co-authorship network of co-authors of Jong Hwan Ko

This figure shows the co-authorship network connecting the top 25 collaborators of Jong Hwan Ko. A scholar is included among the top collaborators of Jong Hwan Ko 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 Jong Hwan Ko. Jong Hwan Ko 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.
Jeon, Kang Eun, et al.. (2025). GAROS: Genetic algorithm-aided row-skipping for shift and duplicate kernel mapping in processing-in-memory architectures. Journal of Systems Architecture. 165. 103423–103423.
2.
Ko, Jong Hwan, et al.. (2024). A DNN partitioning framework with controlled lossy mechanisms for edge-cloud collaborative intelligence. Future Generation Computer Systems. 154. 426–439. 6 indexed citations
3.
4.
Jeon, Kang Eun, et al.. (2024). KERNTROL: Kernel Shape Control Toward Ultimate Memory Utilization for In-Memory Convolutional Weight Mapping. IEEE Transactions on Circuits and Systems I Regular Papers. 71(12). 6138–6151.
5.
Truong, Phuoc Loc, et al.. (2024). Reconfigurable Resistive Switching Memory for Telegraph Code Sensing and Recognizing Reservoir Computing Systems. Small. 20(40). e2402961–e2402961. 5 indexed citations
6.
Park, Juhong, et al.. (2024). An Efficient Ventricular Arrhythmias Detection on Microcontrollers with Optimized 1D CNN. 572–576. 2 indexed citations
7.
Choi, Jaehyuk, et al.. (2024). An FPGA-Based Energy-Efficient Real-Time Hand Pose Estimation System With an Integrated Image Signal Processor for Indirect 3-D Time-of-Flight Sensors. IEEE Internet of Things Journal. 12(2). 1817–1830. 1 indexed citations
8.
Kim, Dohyung, Hyoung Won Baac, Jong‐Min Lee, et al.. (2023). Room‐Temperature‐Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification. Advanced Functional Materials. 33(14). 19 indexed citations
9.
Kim, Dohyung, Phuoc Loc Truong, Jong‐Min Lee, et al.. (2023). Highly Reliable 3D Channel Memory and Its Application in a Neuromorphic Sensory System for Hand Gesture Recognition. ACS Nano. 17(24). 24826–24840. 13 indexed citations
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Moon, Sungmin, et al.. (2022). VWC-SDK: Convolutional Weight Mapping Using Shifted and Duplicated Kernel With Variable Windows and Channels. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 12(2). 408–421. 9 indexed citations
13.
Lee, Joo Chan, et al.. (2022). Scalable Color Quantization for Task-centric Image Compression. ACM Transactions on Multimedia Computing Communications and Applications. 19(2s). 1–18. 9 indexed citations
14.
Biswas, Deblina, Prasanta Dey, Jong Hwan Ko, et al.. (2022). Micro-ultrasonic Assessment of Early Stage Clot Formation and Whole Blood Coagulation Using an All-Optical Ultrasound Transducer and Adaptive Signal Processing Algorithm. ACS Sensors. 7(10). 2940–2950. 7 indexed citations
15.
Lee, Joo Chan, Yongwoo Kim, SungTae Moon, & Jong Hwan Ko. (2021). A Splittable DNN-Based Object Detector for Edge-Cloud Collaborative Real-Time Video Inference. 1–8. 8 indexed citations
16.
Kumar, Teerath, et al.. (2021). Binary-Classifiers-Enabled Filters for Semi-Supervised Learning. IEEE Access. 9. 167663–167673. 18 indexed citations
17.
Ko, Jong Hwan, et al.. (2021). Target-Dependent Scalable Image Compression Using a Reconfigurable Recurrent Neural Network. IEEE Access. 9. 119418–119429. 6 indexed citations
18.
Mudassar, Burhan Ahmad, Yun Long, Taesik Na, et al.. (2019). CAMEL: An Adaptive Camera With Embedded Machine Learning-Based Sensor Parameter Control. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 9(3). 498–508. 11 indexed citations
19.
Ko, Jong Hwan, et al.. (2018). 3-D Stacked Image Sensor With Deep Neural Network Computation. IEEE Sensors Journal. 18(10). 4187–4199. 30 indexed citations
20.

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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