Tong Yu
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 10%
- Surgery
- Biomedical Engineering
- Co-authors
- Ruiyi ZhangJiuxiang GuSungchul KimRyan A. RossiIsabel O. GallegosJoe BarrowFranck DernoncourtNesreen K. Ahmed
- Topics
- Recommender Systems and Techniques (11 papers)Advanced Bandit Algorithms Research (11 papers)Topic Modeling (7 papers)
- Partner nations
- United StatesChinaFrance
In The Last Decade
Tong Yu
49 papers receiving 594 citations
Hit Papers
Peers
Comparison fields: 5 of 102
- Artificial Intelligence 228
- Computer Vision and Pattern Recognition 188
- Information Systems 92
- Surgery 81
- Biomedical Engineering 55
Countries citing papers authored by Tong Yu
This map shows the geographic impact of Tong Yu'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 Tong Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tong Yu more than expected).
Fields of papers citing papers by Tong Yu
This network shows the impact of papers produced by Tong Yu. 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 Tong Yu. The network helps show where Tong Yu may publish in the future.
Co-authorship network of co-authors of Tong Yu
This figure shows the co-authorship network connecting the top 25 collaborators of Tong Yu. A scholar is included among the top collaborators of Tong Yu 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 Tong Yu. Tong Yu 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 | 0 | |
| 3 | 4 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 7 | |
| 8 | 6 | |
| 9 | 3 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | 1 | |
| 13 | 1 | |
| 14 | 28 | |
| 15 | Graphical Models Meet Bandits: A Variational Thompson Sampling Approach. | 2 |
| 16 | ILPC: Iterative Learning Using Physical Constraints in Real-World Sensing Data. | 3 |
| 17 | 2 | |
| 18 | Markov Chain analysis of noise and restart in stochastic local search | 4 |
| 19 | Improving Demand Prediction in Bike Sharing System by Learning Global Features | 15 |
| 20 | 1 |
About Tong Yu
Tong Yu is a scholar working on Health Informatics, Artificial Intelligence and Management Science and Operations Research, having authored 61 papers that have together received 618 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (11 papers), Advanced Bandit Algorithms Research (11 papers) and Topic Modeling (7 papers). The work is most often cited by research in Health Informatics (38 citations), Computer Vision and Pattern Recognition (188 citations) and Artificial Intelligence (228 citations). Tong Yu has collaborated with scholars based in United States, China and France. Frequent co-authors include Ruiyi Zhang, Jiuxiang Gu, Sungchul Kim, Ryan A. Rossi, Isabel O. Gallegos, Joe Barrow, Franck Dernoncourt, Nesreen K. Ahmed, Md Mehrab Tanjim and Nicolas Padoy. Their work appears in journals such as Biology of Reproduction, IEEE Transactions on Knowledge and Data Engineering and Neural Networks.
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.