Michael Burke

5.1k citations
115 papers · 2.3k · h-index 26

Impact in

Papers in

Michael Burke

110 papers receiving 2.1k citations

Peers

Michael Burke
Comparison fields: 5 of 147
  • Hardware and Architecture 988
  • Software 534
  • Computer Networks and Communications 685
  • Artificial Intelligence 954
  • Information Systems 450
Replace Wolfgang Schröder‐Preikschat with:
Wolfgang Schröder‐Preikschat Germany
Craig Zilles United States
Philip K. McKinley United States
Richard C. Waters United States
Pedro Peris‐Lopez Spain
Rüdiger Kapitza Germany
Nan Guan China
Murali Annavaram United States
Ayan Banerjee United States
J.H. Aylor United States
Michael Burke relative to Wolfgang Schröder‐Preikschat Germany Wolfgang Schröder‐Preikschat's profile →
Citations per field
00.5×
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 115 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1993249
2 1999196
3 1999135
4 1988113
5 1986104
6 199096
7 201793
8 201087
9 200480
10 198261
11 199355
12 201355
13 198652
14 200050
15 200447
16 198846
17 201843
18 199337
19 198733
20 200532

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 115 papers that have together received 2.3k indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (23 papers), Logic, programming, and type systems (15 papers), Natural Language Processing Techniques (13 papers), Topic Modeling (10 papers), Software Testing and Debugging Techniques (8 papers), Distributed and Parallel Computing Systems (8 papers), Formal Methods in Verification (7 papers) and Advanced Database Systems and Queries (6 papers). The work is most often cited by research in Hardware and Architecture (988 citations), Software (534 citations), Computer Networks and Communications (685 citations), Artificial Intelligence (954 citations) and Information Systems (450 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, Jeanne Ferrante, Vugranam C. Sreedhar, Josef van Genabith, Aoife Cahill and Ruth O'Donovan. Their work appears in journals such as ACM SIGPLAN Notices, ACM Transactions on Programming Languages and Systems, IEEE Robotics and Automation Letters, 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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