Jin Feng

1.1k total citations
18 papers, 483 citations indexed

About

Jin Feng is a scholar working on Finance, Applied Mathematics and Statistics and Probability. According to data from OpenAlex, Jin Feng has authored 18 papers receiving a total of 483 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Finance, 6 papers in Applied Mathematics and 4 papers in Statistics and Probability. Recurrent topics in Jin Feng's work include Stochastic processes and financial applications (7 papers), Geometric Analysis and Curvature Flows (6 papers) and Navier-Stokes equation solutions (3 papers). Jin Feng is often cited by papers focused on Stochastic processes and financial applications (7 papers), Geometric Analysis and Curvature Flows (6 papers) and Navier-Stokes equation solutions (3 papers). Jin Feng collaborates with scholars based in United States, China and Japan. Jin Feng's co-authors include Thomas G. Kurtz, David Nualart, Luigi Ambrosio, Markos A. Katsoulakis, Martin Forde, Jean‐Pierre Fouque, Truyen Nguyen, Rohini Kumar, Andrzej Święch and Eulàlia Nualart and has published in prestigious journals such as Statistics in Medicine, Communications in Mathematical Physics and Transactions of the American Mathematical Society.

In The Last Decade

Jin Feng

18 papers receiving 447 citations

Peers

Jin Feng
Comparison fields: 5 of 50
  • Finance 255
  • Applied Mathematics 176
  • Mathematical Physics 128
  • Statistical and Nonlinear Physics 80
  • Computational Theory and Mathematics 76
Replace Benjamin Jourdain with:
Benjamin Jourdain France
Bernard Roynette France
Huaizhong Zhao United Kingdom
Jinghai Shao China
N. Krylov United States
Seiichiro Kusuoka Japan
Laure Coutin France
Tom Lindstrøm Norway
Frank Proske Norway
A. Mazel United States
Benjamin Jourdain France View profile →
Citations per field, relative to Jin Feng
Jin Feng · 1×
Citations per year, relative to Jin Feng
Jin Feng · 1×

Countries citing papers authored by Jin Feng

Since Specialization
Citations

This map shows the geographic impact of Jin Feng'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 Jin Feng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jin Feng more than expected).

Fields of papers citing papers by Jin Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jin Feng. 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 Jin Feng. The network helps show where Jin Feng may publish in the future.

Co-authorship network of co-authors of Jin Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Jin Feng. A scholar is included among the top collaborators of Jin Feng 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 Jin Feng. Jin Feng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
# Work Indexed citations
1 8
2 38
3 4
4 1
5 13
6 17
7 30
8 3
9 22
10 6
11 1
12 35
13 73
14 32
15 176
16 6
17 6
18 12

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.

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