Huichen Dai

1.1k citations
42 papers · 867 · h-index 15

Impact in

Papers in

Huichen Dai

39 papers receiving 830 citations

Peers

Huichen Dai
Comparison fields: 5 of 26
  • Computer Networks and Communications 850
  • Hardware and Architecture 229
  • Computational Mathematics 4
  • Materials Chemistry 241
  • Artificial Intelligence 99
Replace Jianyuan Lu with:
Jianyuan Lu China
Andrey Ermolinskiy United States
Somaya Arianfar Finland
Xiaowei Shen United States
Claudio Imbrenda Germany
Massimo Gallo France
Ashish Kumar Singh India
Boning Feng Norway
Christos Tsilopoulos Greece
Huichen Dai relative to Jianyuan Lu China Jianyuan Lu's profile →
Citations per field
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Jianyuan Lu · 1×
Citations per year

Countries citing papers authored by Huichen Dai

Since Specialization
Citations

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

Fields of papers citing papers by Huichen Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Huichen Dai, 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 Huichen Dai Line = papers co-authored together Huichen Dai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012116
2 2013106
3
Wire speed name lookup: a GPU-based approach
201384
4 201378
5 201269
6 200438
7 201436
8 201631
9 201230
10 201128
11 201525
12 201622
13 201421
14 201521
15 201316
16 201314
17 201812
18 202112
19 201810
20 201710

About Huichen Dai

Huichen Dai is a scholar working on Computer Networks and Communications, Hardware and Architecture, Materials Chemistry, Information Systems and Artificial Intelligence, having authored 42 papers that have together received 867 indexed citations. Recurring topics across this work include Caching and Content Delivery (28 papers), Network Packet Processing and Optimization (16 papers), Advanced Data Storage Technologies (12 papers), Software-Defined Networks and 5G (11 papers), Cooperative Communication and Network Coding (10 papers), Covalent Organic Framework Applications (10 papers), Interconnection Networks and Systems (7 papers) and Cloud Computing and Resource Management (3 papers). The work is most often cited by research in Computer Networks and Communications (850 citations), Hardware and Architecture (229 citations), Computational Mathematics (4 citations), Materials Chemistry (241 citations) and Artificial Intelligence (99 citations). Huichen Dai has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Yi Wang, Bin Liu, Jianyuan Lu, Bin Liu, Ting Zhang, Keqiang He, Bin Liu, Junchen Jiang, Tian Pan and Wei Meng. Their work appears in journals such as IEEE/ACM Transactions on Networking, Computer Networks, IEEE Transactions on Parallel and Distributed Systems, ACM SIGMETRICS Performance Evaluation Review and Journal of Communications.

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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