Matthew Burgess
- Artificial Intelligence top 10%
- Topic Modeling 1
- Semantic Web and Ontologies 1
- Information Systems top 10%
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- Complex Network Analysis Techniques 2
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- Early Childhood Education and Development 1
- Parental Involvement in Education 1
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- Biomedical Text Mining and Ontologies 1
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- Oil and Gas Production Techniques 1
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- Educational and Psychological Assessments 1
- Co-authors
- Natasha NoyDan BrickleyMichael CafarellaEytan AdarFeng NiuYongjoo ParkCe ZhangMichael R. Anderson
- Cited by
- Management Science and Operations ResearchInformation Systems and ManagementArtificial Intelligence
- Journals
- PLoS ONE (1 paper)Conference on Innovative Data Systems Research (1 paper)Proceedings of the International AAAI Conference on Web and Social Media (1 paper)
- Partner nations
- United States
In The Last Decade
Matthew Burgess
6 papers receiving 219 citations
Peers
Comparison fields: 5 of 46
- Management Science and Operations Research 71
- Information Systems and Management 37
- Artificial Intelligence 152
- Information Systems 84
- Signal Processing 33
Countries citing papers authored by Matthew Burgess
This map shows the geographic impact of Matthew Burgess'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 Matthew Burgess with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Burgess more than expected).
Fields of papers citing papers by Matthew Burgess
This network shows the impact of papers produced by Matthew Burgess. 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 Matthew Burgess. The network helps show where Matthew Burgess may publish in the future.
Co-authorship network
The 12 scholars most cited alongside Matthew Burgess, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 0 | |
| 2 | 2023 | 1 | |
| 3 | 2021 | 1 | |
| 4 | 2019 | 131 | |
| 5 | 2016 | 27 | |
| 6 | 2016 | 11 | |
| 7 | Brainwash: A data system for feature engineering | 2013 | 68 |
About Matthew Burgess
Matthew Burgess is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Information Systems and Management, having authored 7 papers that have together received 239 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (2 papers), Early Childhood Education and Development (1 paper), Biomedical Text Mining and Ontologies (1 paper), Parental Involvement in Education (1 paper), Oil and Gas Production Techniques (1 paper), Educational and Psychological Assessments (1 paper), Topic Modeling (1 paper) and Semantic Web and Ontologies (1 paper). The work is most often cited by research in Management Science and Operations Research (71 citations), Information Systems and Management (37 citations) and Artificial Intelligence (152 citations). Matthew Burgess has collaborated with scholars based in United States. Frequent co-authors include Natasha Noy, Dan Brickley, Michael Cafarella, Eytan Adar, Feng Niu, Yongjoo Park, Ce Zhang, Michael R. Anderson, Christopher Ré and Victor Bittorf. Their work appears in journals such as PLoS ONE, Conference on Innovative Data Systems Research and Proceedings of the International AAAI Conference on Web and Social Media.
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.