Ernie Chang
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Speech and dialogue systems
- Speech Recognition and Synthesis
- Advanced Text Analysis Techniques
- Sentiment Analysis and Opinion Mining
- Semantic Web and Ontologies
Papers in
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- Natural Language Processing Techniques 13
- Topic Modeling 11
- Speech Recognition and Synthesis 5
-
- Multimodal Machine Learning Applications 5
- Music Technology and Sound Studies 3
- Co-authors
- Vera Demberg (9 shared papers)Xiaoyu Shen (4 shared papers)Vikas Chandra (8 shared papers)Alex Marin (2 shared papers)Hui Su (2 shared papers)Barlas Oğuz (1 shared paper)Yangyang Shi (1 shared paper)Zechun Liu (2 shared papers)
- Journals
- BioMedInformatics (3 papers)INFM-OAR (INFN Catania) (1 paper)TSpace (1 paper)
- Partner nations
- GermanyUnited StatesSouth Korea
In The Last Decade
Ernie Chang
21 papers receiving 171 citations
Peers
Comparison fields: 5 of 44
- Artificial Intelligence 139
- Health Informatics 4
- Computer Vision and Pattern Recognition 46
- Computational Mathematics 1
- General Social Sciences 4
Countries citing papers authored by Ernie Chang
This map shows the geographic impact of Ernie Chang'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 Ernie Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ernie Chang more than expected).
Fields of papers citing papers by Ernie Chang
This network shows the impact of papers produced by Ernie Chang. 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 Ernie Chang. The network helps show where Ernie Chang may publish in the future.
Co-authors
The 25 scholars most cited alongside Ernie Chang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 51 | |
| 2 | 2021 | 26 | |
| 3 | 2021 | 14 | |
| 4 | 2021 | 14 | |
| 5 | 2021 | 12 | |
| 6 | 2018 | 11 | |
| 7 | 2020 | 10 | |
| 8 | 2021 | 8 | |
| 9 | 2020 | 8 | |
| 10 | 2024 | 6 | |
| 11 | 2021 | 5 | |
| 12 | 2019 | 4 | |
| 13 | 2024 | 3 | |
| 14 | 2011 | 3 | |
| 15 | 2024 | 2 | |
| 16 | 2024 | 1 | |
| 17 | 2023 | 1 | |
| 18 | 2023 | 1 | |
| 19 | 2024 | 1 | |
| 20 | 2024 | 1 |
About Ernie Chang
Ernie Chang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, General Health Professions and Epidemiology, having authored 25 papers that have together received 184 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (13 papers), Topic Modeling (11 papers), Multimodal Machine Learning Applications (5 papers), Music and Audio Processing (5 papers), Speech Recognition and Synthesis (5 papers), Music Technology and Sound Studies (3 papers), Speech and Audio Processing (2 papers) and Microgrid Control and Optimization (1 paper). The work is most often cited by research in Artificial Intelligence (139 citations), Health Informatics (4 citations), Computer Vision and Pattern Recognition (46 citations), Computational Mathematics (1 citation) and General Social Sciences (4 citations). Ernie Chang has collaborated with scholars based in Germany, United States and South Korea. Frequent co-authors include Vera Demberg, Xiaoyu Shen, Vikas Chandra, Alex Marin, Hui Su, Barlas Oğuz, Yangyang Shi, Zechun Liu, Yashar Mehdad and Pierre Stock. Their work appears in journals such as BioMedInformatics, INFM-OAR (INFN Catania) and TSpace.
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.