Di Jin
- Artificial Intelligence top 1%
- Aerospace Engineering top 2%
- Computational Mechanics top 2%
- Computer Vision and Pattern Recognition top 5%
- Atomic and Molecular Physics, and Optics top 10%
- Co-authors
- Peter SzolovitsZhijing JinJoey Tianyi ZhouPeter T. C. SoZahid YaqoobYun WuRenjie ZhouMin Jia
- Topics
- Topic Modeling (31 papers)Plasma and Flow Control in Aerodynamics (24 papers)Natural Language Processing Techniques (20 papers)
- Journals
- SHILAP Revista de lepidopterologíaApplied Physics LettersBioinformatics
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Di Jin
112 papers receiving 2.5k citations
Hit Papers
Peers
Comparison fields: 5 of 133
- Artificial Intelligence 1.3k
- Aerospace Engineering 644
- Computational Mechanics 379
- Computer Vision and Pattern Recognition 289
- Atomic and Molecular Physics, and Optics 285
Countries citing papers authored by Di Jin
This map shows the geographic impact of Di Jin'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 Di Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Di Jin more than expected).
Fields of papers citing papers by Di Jin
This network shows the impact of papers produced by Di Jin. 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 Di Jin. The network helps show where Di Jin may publish in the future.
Co-authorship network of co-authors of Di Jin
This figure shows the co-authorship network connecting the top 25 collaborators of Di Jin. A scholar is included among the top collaborators of Di Jin 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 Di Jin. Di Jin 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 | 0 | |
| 4 | 5 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 7 | |
| 8 | 1 | |
| 9 | 7 | |
| 10 | 6 | |
| 11 | 10 | |
| 12 | Deep Learning for Text Attribute Transfer: A Survey | 1 |
| 13 | 68 | |
| 14 | Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment | 83 |
| 15 | Unsupervised Text Style Transfer via Iterative Matching and Translation. | 5 |
| 16 | 37 | |
| 17 | Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention | 5 |
| 18 | Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning | 28 |
| 19 | 142 | |
| 20 | A Hybrid Approach to Precision Medicine-related Biomedical Article Retrieval and Clinical Trial Matching. | 2 |
About Di Jin
Di Jin is a scholar working on Aerospace Engineering, Biophysics and Artificial Intelligence, having authored 122 papers that have together received 2.7k indexed citations. Recurring topics across this work include Topic Modeling (31 papers), Plasma and Flow Control in Aerodynamics (24 papers) and Natural Language Processing Techniques (20 papers). The work is most often cited by research in Health Informatics (109 citations), Artificial Intelligence (1.3k citations) and Biophysics (163 citations). Di Jin has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Peter Szolovits, Zhijing Jin, Joey Tianyi Zhou, Peter T. C. So, Zahid Yaqoob, Yun Wu, Renjie Zhou, Min Jia, Eileen Pan and Wei‐Hung Weng. Their work appears in journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and Bioinformatics.
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.