Chang Kong
- Electrical and Electronic Engineering
- Renewable Energy, Sustainability and the Environment top 10%
- Materials Chemistry
- Biomedical Engineering
- Molecular Biology
- Co-authors
- Han Kyu LeeTae-Hee LeeDo Y. KimYong‐Gun ShulKrishna RajanScott BroderickShuichi IwataYashdeep Phanse
- Topics
- Machine Learning in Materials Science (5 papers)Generative Adversarial Networks and Image Synthesis (3 papers)Electrocatalysts for Energy Conversion (3 papers)
- Cited by
- Renewable Energy, Sustainability and the EnvironmentElectrical and Electronic EngineeringMaterials Chemistry
- Partner nations
- United StatesChinaJapan
In The Last Decade
Chang Kong
20 papers receiving 540 citations
Peers
Comparison fields: 5 of 82
- Electrical and Electronic Engineering 295
- Renewable Energy, Sustainability and the Environment 236
- Materials Chemistry 182
- Biomedical Engineering 92
- Molecular Biology 60
Countries citing papers authored by Chang Kong
This map shows the geographic impact of Chang Kong'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 Chang Kong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chang Kong more than expected).
Fields of papers citing papers by Chang Kong
This network shows the impact of papers produced by Chang Kong. 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 Chang Kong. The network helps show where Chang Kong may publish in the future.
Co-authorship network of co-authors of Chang Kong
This figure shows the co-authorship network connecting the top 25 collaborators of Chang Kong. A scholar is included among the top collaborators of Chang Kong 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 Chang Kong. Chang Kong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 25 | |
| 8 | 8 | |
| 9 | 19 | |
| 10 | 4 | |
| 11 | 11 | |
| 12 | 0 | |
| 13 | 25 | |
| 14 | 8 | |
| 15 | 33 | |
| 16 | 64 | |
| 17 | 3 | |
| 18 | 16 | |
| 19 | 23 | |
| 20 | 290 |
About Chang Kong
Chang Kong is a scholar working on Hardware and Architecture, Computational Theory and Mathematics and Dermatology, having authored 23 papers that have together received 554 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (5 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Electrocatalysts for Energy Conversion (3 papers). The work is most often cited by research in Renewable Energy, Sustainability and the Environment (236 citations), Electrical and Electronic Engineering (295 citations) and Materials Chemistry (182 citations). Chang Kong has collaborated with scholars based in United States, China and Japan. Frequent co-authors include Han Kyu Lee, Tae-Hee Lee, Do Y. Kim, Yong‐Gun Shul, Krishna Rajan, Krishna Rajan, Scott Broderick, Shuichi Iwata, Yashdeep Phanse and Michael J. Wannemuehler. Their work appears in journals such as Advanced Functional Materials, Journal of Power Sources and Scientific Reports.
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.