Dan Chen

223 papers receiving 4.8k citations

Peers

Dan Chen
Comparison fields: 5 of 202
  • Computational Mathematics 46
  • Computer Networks and Communications 853
  • Cognitive Neuroscience 627
  • Information Systems 763
  • Pollution 315
Replace Rupesh K. Srivastava with:
Rupesh K. Srivastava India
Huihui Wang China
Toshihiko Yanase Japan
Hongzhi Wang China
Lifeng Wang China
Xiang Li China
Yang Li China
Ya Zhang China
Ping Li China
Olga G. Troyanskaya United States
Dan Chen relative to Rupesh K. Srivastava India Rupesh K. Srivastava's profile →
Citations per field
00.5×6.6×
Rupesh K. Srivastava · 1×
Citations per year

Countries citing papers authored by Dan Chen

Since Specialization
Citations

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

Fields of papers citing papers by Dan Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Dan Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dan Chen Line = papers co-authored together Dan Chen links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 242 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004454
2 2012266
3 2013162
4 2008122
5 2019120
6 2017113
7 201098
8 201597
9 201286
10 201282
11 201381
12 202379
13 201876
14 201576
15 201476
16 201362
17 201961
18
Reconstruction of damaged cornea by autologous transplantation of epidermal adult stem cells.
200859
19 201259
20 202056

About Dan Chen

Dan Chen is a scholar working on Cognitive Neuroscience, Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition and Signal Processing, having authored 242 papers that have together received 4.9k indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (26 papers), Distributed and Parallel Computing Systems (17 papers), Cloud Computing and Resource Management (12 papers), Blind Source Separation Techniques (12 papers), Wastewater Treatment and Nitrogen Removal (11 papers), Neural dynamics and brain function (10 papers), Sleep and Wakefulness Research (9 papers) and Parallel Computing and Optimization Techniques (9 papers). The work is most often cited by research in Computational Mathematics (46 citations), Computer Networks and Communications (853 citations), Cognitive Neuroscience (627 citations), Information Systems (763 citations) and Pollution (315 citations). Dan Chen has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Lizhe Wang, Xiaoli Li, Rajiv Ranjan, Hongyu Wang, Kai Yang, Albert Y. Zomaya, Jingying Chen, Samee U. Khan, Yunbo Tang and Jie Tao. Their work appears in journals such as Neurocomputing, IEEE Transactions on Parallel and Distributed Systems, Future Generation Computer Systems, Computers & Electrical Engineering and Neurology.

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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