Michael Le

528 citations
21 papers · 371 · h-index 8

Impact in

Papers in

Michael Le

19 papers receiving 343 citations

Peers

Michael Le
Comparison fields: 5 of 34
  • Computer Networks and Communications 305
  • Information Systems 158
  • Hardware and Architecture 25
  • Software 11
  • Electrical and Electronic Engineering 122
Replace Venkat Arun with:
Venkat Arun India
Daniel Turner United States
Ruxandra F. Olimid Romania
P. Spilling Norway
Sadjad Fouladi United States
Ruan He France
Dennis Cai United States
Jonghwan Hyun South Korea
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Citations per field
00.5×9.5×
Venkat Arun · 1×
Citations per year

Countries citing papers authored by Michael Le

Since Specialization
Citations

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

Fields of papers citing papers by Michael Le

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016138
2 200889
3 200634
4 201124
5 202316
6 201415
7 20239
8 20098
9 20107
10 20235
11 20225
12 20095
13 20114
14 20124
15 20113
16 20232
17 20251
18 20181
19 20231
20 20250

About Michael Le

Michael Le is a scholar working on Computer Networks and Communications, Artificial Intelligence, Information Systems, Electrical and Electronic Engineering and Signal Processing, having authored 21 papers that have together received 371 indexed citations. Recurring topics across this work include Security and Verification in Computing (8 papers), Cloud Computing and Resource Management (7 papers), Software System Performance and Reliability (6 papers), Distributed systems and fault tolerance (5 papers), Advanced Malware Detection Techniques (4 papers), Software-Defined Networks and 5G (3 papers), Opportunistic and Delay-Tolerant Networks (3 papers) and Radiation Effects in Electronics (3 papers). The work is most often cited by research in Computer Networks and Communications (305 citations), Information Systems (158 citations), Hardware and Architecture (25 citations), Software (11 citations) and Electrical and Electronic Engineering (122 citations). Michael Le has collaborated with scholars based in United States, South Korea and Germany. Frequent co-authors include Tao Shu, Hui Kang, Mário Gerla, Yuval Tamir, Jérôme Härri, Kevin C. Lee, Joon‐Sang Park, Dan Williams, A. Oswald and Sahil Suneja. Their work appears in journals such as ACM SIGPLAN Notices, Neurology, IEEE Transactions on Dependable and Secure Computing, IEEE Vehicular Technology Magazine and Lecture notes in computer science.

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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