Michael Burke

5.1k citations
128 papers · 2.9k · h-index 30

Impact in

Papers in

Michael Burke

121 papers receiving 2.6k citations

Peers

Michael Burke
Comparison fields: 5 of 146
  • Hardware and Architecture 1.2k
  • Software 683
  • Artificial Intelligence 1.2k
  • Computer Networks and Communications 806
  • Information Systems 556
Replace Wolfgang Schröder‐Preikschat with:
Wolfgang Schröder‐Preikschat Germany
Richard C. Waters United States
Craig Zilles United States
Masahide Nakamura Japan
Ayan Banerjee United States
Robert C. Miller United States
Rüdiger Kapitza Germany
Philip K. McKinley United States
Dan R. Olsen United States
Slim Abdennadher Egypt
Michael Burke relative to Wolfgang Schröder‐Preikschat Germany Wolfgang Schröder‐Preikschat's profile →
Citations per field
00.5×11×
Wolfgang Schröder‐Preikschat · 1×
Citations per year

Countries citing papers authored by Michael Burke

Since Specialization
Citations

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

Fields of papers citing papers by Michael Burke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 128 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1993295
2 1999220
3 1999168
4 1988126
5 1986124
6 2017115
7 1990109
8 2010101
9 199582
10 200481
11 198264
12 198663
13 201362
14 200859
15 200059
16 198859
17 199359
18 200458
19 200556
20 200546

About Michael Burke

Michael Burke is a scholar working on Artificial Intelligence, Hardware and Architecture, Computer Networks and Communications, Computer Vision and Pattern Recognition and Software, having authored 128 papers that have together received 2.9k indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (25 papers), Logic, programming, and type systems (17 papers), Natural Language Processing Techniques (16 papers), Topic Modeling (11 papers), Software Testing and Debugging Techniques (10 papers), Distributed and Parallel Computing Systems (8 papers), Advanced Database Systems and Queries (7 papers) and Formal Methods in Verification (7 papers). The work is most often cited by research in Hardware and Architecture (1.2k citations), Software (683 citations), Artificial Intelligence (1.2k citations), Computer Networks and Communications (806 citations) and Information Systems (556 citations). Michael Burke has collaborated with scholars based in United States, South Africa and United Kingdom. Frequent co-authors include Jong-Deok Choi, Ron K. Cytron, Paul Carini, Michael Hind, Vivek Sarkar, Andy Way, Ruth O'Donovan, Jeanne Ferrante, Aoife Cahill and Josef van Genabith. Their work appears in journals such as ACM SIGPLAN Notices, IEEE Robotics and Automation Letters, ACM Transactions on Programming Languages and Systems, Computational Linguistics and Hospital Pediatrics.

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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