Yujia Li
- Artificial Intelligence top 2%
- Molecular Biology
- Computer Vision and Pattern Recognition top 2%
- Cancer Research top 10%
- Plant Science
- Co-authors
- Richard S. ZemelRaquel UrtasunWenjie LuoJiangjuan ShaoMin ShenZili ZhangYingqian WangAnping Chen
- Topics
- Advanced Graph Neural Networks (7 papers)Topic Modeling (6 papers)RNA modifications and cancer (5 papers)
- Journals
- Nature CommunicationsSHILAP Revista de lepidopterologíaEnvironmental Science & Technology
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Yujia Li
67 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 168
- Artificial Intelligence 497
- Molecular Biology 475
- Computer Vision and Pattern Recognition 442
- Cancer Research 189
- Plant Science 142
Countries citing papers authored by Yujia Li
This map shows the geographic impact of Yujia Li'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 Yujia Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yujia Li more than expected).
Fields of papers citing papers by Yujia Li
This network shows the impact of papers produced by Yujia Li. 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 Yujia Li. The network helps show where Yujia Li may publish in the future.
Co-authorship network of co-authors of Yujia Li
This figure shows the co-authorship network connecting the top 25 collaborators of Yujia Li. A scholar is included among the top collaborators of Yujia Li 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 Yujia Li. Yujia Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 0 | |
| 8 | 3 | |
| 9 | 7 | |
| 10 | 1 | |
| 11 | 31 | |
| 12 | 43 | |
| 13 | 45 | |
| 14 | 5 | |
| 15 | Text Recognition using local correlation. | 1 |
| 16 | REGAL: Transfer Learning For Fast Optimization of Computation Graphs. | 5 |
| 17 | Screening and identification of peach endophytic bacteria with antagonism against Agrobacterium tumefaciens. | 6 |
| 18 | Imagination-Augmented Agents for Deep Reinforcement Learning | 49 |
| 19 | High Order Regularization for Semi-Supervised Learning of Structured Output Problems | 12 |
| 20 | Celebrity recommendation with collaborative social topic regression | 15 |
About Yujia Li
Yujia Li is a scholar working on Computer Vision and Pattern Recognition, Endocrinology and Artificial Intelligence, having authored 86 papers that have together received 1.8k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (7 papers), Topic Modeling (6 papers) and RNA modifications and cancer (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (442 citations), Artificial Intelligence (497 citations) and Cancer Research (189 citations). Yujia Li has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Richard S. Zemel, Raquel Urtasun, Wenjie Luo, Jiangjuan Shao, Min Shen, Zili Zhang, Yingqian Wang, Anping Chen, Guo-Ping Yin and Shizhong Zheng. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and Environmental Science & Technology.
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.