Jian Ding
- Mathematical Physics top 2%
- Statistics and Probability top 2%
- Condensed Matter Physics top 10%
- Artificial Intelligence
- Computational Theory and Mathematics top 5%
- Topics
- Stochastic processes and statistical mechanics (28 papers)Markov Chains and Monte Carlo Methods (19 papers)Theoretical and Computational Physics (12 papers)
- Journals
- SHILAP Revista de lepidopterologíaJNCI Journal of the National Cancer InstituteInternational Journal of Radiation Oncology*Biology*Physics
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Jian Ding
53 papers receiving 656 citations
Peers
Comparison fields: 5 of 88
- Mathematical Physics 335
- Statistics and Probability 248
- Condensed Matter Physics 132
- Artificial Intelligence 87
- Computational Theory and Mathematics 85
Countries citing papers authored by Jian Ding
This map shows the geographic impact of Jian Ding'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 Jian Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jian Ding more than expected).
Fields of papers citing papers by Jian Ding
This network shows the impact of papers produced by Jian Ding. 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 Jian Ding. The network helps show where Jian Ding may publish in the future.
Co-authorship network of co-authors of Jian Ding
This figure shows the co-authorship network connecting the top 25 collaborators of Jian Ding. A scholar is included among the top collaborators of Jian Ding 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 Jian Ding. Jian Ding 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 | 0 | |
| 3 | 2 | |
| 4 | 5 | |
| 5 | 10 | |
| 6 | 1 | |
| 7 | 2 | |
| 8 | Accuracy Analysis for 6-DOF PKM with Sobol Sequence Based Quasi Monte Carlo Method | 1 |
| 9 | 10 | |
| 10 | 49 | |
| 11 | On multiple peaks and moderate large deviations of Gaussian fields | 1 |
| 12 | 15 | |
| 13 | 8 | |
| 14 | 1 | |
| 15 | 1 | |
| 16 | 60 | |
| 17 | 3 | |
| 18 | 12 | |
| 19 | 34 | |
| 20 | 51 |
About Jian Ding
Jian Ding is a scholar working on Mathematical Physics, Statistics and Probability and Discrete Mathematics and Combinatorics, having authored 55 papers that have together received 688 indexed citations. Recurring topics across this work include Stochastic processes and statistical mechanics (28 papers), Markov Chains and Monte Carlo Methods (19 papers) and Theoretical and Computational Physics (12 papers). The work is most often cited by research in Mathematical Physics (335 citations), Statistics and Probability (248 citations) and Discrete Mathematics and Combinatorics (57 citations). Jian Ding has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Yuval Peres, Eyal Lubetzky, Nike Sun, Ofer Zeitouni, Allan Sly, Maury Bramson, James C. Lee, Chaosu Hu, Jeong Han Kim and Fuxi Zhang. Their work appears in journals such as SHILAP Revista de lepidopterología, JNCI Journal of the National Cancer Institute and International Journal of Radiation Oncology*Biology*Physics.
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.