Siqu Long
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
- Natural Language Processing Techniques
- Speech and dialogue systems
- Hate Speech and Cyberbullying Detection
- Anomaly Detection Techniques and Applications
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- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
Papers in
-
- Topic Modeling 4
- Natural Language Processing Techniques 3
- Domain Adaptation and Few-Shot Learning 3
-
- Biomedical Text Mining and Ontologies 3
- Single-cell and spatial transcriptomics 2
- Co-authors
- Soyeon Caren Han (11 shared papers)Josiah Poon (8 shared papers)Xiaojun Wan (1 shared paper)Haiqin Yang (1 shared paper)Dong Lu (1 shared paper)Huichun Li (1 shared paper)Chunlei Liu (2 shared papers)Pengyi Yang (2 shared papers)
- Journals
- Nature Methods (1 paper)Bioinformatics (1 paper)ACM Computing Surveys (1 paper)Frontiers in Digital Health (1 paper)Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (1 paper)
- Partner nations
- AustraliaUnited KingdomChina
In The Last Decade
Siqu Long
11 papers receiving 177 citations
Peers
Comparison fields: 5 of 47
- Artificial Intelligence 118
- Computer Vision and Pattern Recognition 75
- Information Systems 18
- Software 3
- Health Informatics 1
Countries citing papers authored by Siqu Long
This map shows the geographic impact of Siqu Long'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 Siqu Long with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Siqu Long more than expected).
Fields of papers citing papers by Siqu Long
This network shows the impact of papers produced by Siqu Long. 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 Siqu Long. The network helps show where Siqu Long may publish in the future.
Co-authors
The 12 scholars most cited alongside Siqu Long, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 60 | |
| 2 | 2022 | 36 | |
| 3 | 2022 | 26 | |
| 4 | 2020 | 14 | |
| 5 | 2020 | 10 | |
| 6 | 2023 | 9 | |
| 7 | 2021 | 8 | |
| 8 | 2024 | 7 | |
| 9 | 2023 | 6 | |
| 10 | 2023 | 2 | |
| 11 | 2023 | 1 | |
| 12 | 2022 | 0 | |
| 13 | 2025 | 0 |
About Siqu Long
Siqu Long is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Signal Processing and Infectious Diseases, having authored 13 papers that have together received 179 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (4 papers), Advanced Image and Video Retrieval Techniques (4 papers), Topic Modeling (4 papers), Natural Language Processing Techniques (3 papers), Biomedical Text Mining and Ontologies (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Single-cell and spatial transcriptomics (2 papers) and Video Analysis and Summarization (2 papers). The work is most often cited by research in Artificial Intelligence (118 citations), Computer Vision and Pattern Recognition (75 citations), Information Systems (18 citations), Software (3 citations) and Health Informatics (1 citation). Siqu Long has collaborated with scholars based in Australia, United Kingdom and China. Frequent co-authors include Soyeon Caren Han, Josiah Poon, Xiaojun Wan, Haiqin Yang, Dong Lu, Huichun Li, Chunlei Liu, Pengyi Yang, Shila Ghazanfar and Hani Jieun Kim. Their work appears in journals such as Nature Methods, Bioinformatics, ACM Computing Surveys, Frontiers in Digital Health and Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.
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.