Chris Ré
Impact in
- Signal Processing top 5%
- Data Management and Algorithms
- Artificial Intelligence top 10%
- Topic Modeling
- Semantic Web and Ontologies
- Natural Language Processing Techniques
- Bayesian Modeling and Causal Inference
- Machine Learning and Data Classification
Papers in
-
- Topic Modeling 3
- Semantic Web and Ontologies 2
- Data Stream Mining Techniques 1
- Bayesian Modeling and Causal Inference 1
- Imbalanced Data Classification Techniques 1
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- Data Quality and Management 3
- Co-authors
- Dan Suciu (1 shared paper)Bhushan Mandhani (1 shared paper)Nilesh Dalvi (1 shared paper)Stephen H. Bach (2 shared papers)Alexander Ratner (1 shared paper)Henry R. Ehrenberg (1 shared paper)Alex Ratner (3 shared papers)Chong Luo (1 shared paper)
- Journals
- BMC Bioinformatics (1 paper)Communications of the ACM (1 paper)PubMed (1 paper)
- Partner nations
- United StatesLebanon
In The Last Decade
Chris Ré
7 papers receiving 219 citations
Peers
Comparison fields: 5 of 45
- Signal Processing 109
- Artificial Intelligence 152
- Computer Networks and Communications 98
- Management Science and Operations Research 41
- Information Systems 47
Countries citing papers authored by Chris Ré
This map shows the geographic impact of Chris Ré'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 Chris Ré with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris Ré more than expected).
Fields of papers citing papers by Chris Ré
This network shows the impact of papers produced by Chris Ré. 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 Chris Ré. The network helps show where Chris Ré may publish in the future.
Co-authors
The 22 scholars most cited alongside Chris Ré, 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 | 2005 | 129 | |
| 2 | 2019 | 46 | |
| 3 | 2017 | 40 | |
| 4 | 2020 | 15 | |
| 5 | Creating Robust Relation Extract and Anomaly Detect via Probabilistic Logic-Based Reasoning and Learning | 2017 | 2 |
| 6 | 2018 | 1 | |
| 7 | 2024 | 1 |
About Chris Ré
Chris Ré is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Molecular Biology and Information Systems, having authored 7 papers that have together received 234 indexed citations. Recurring topics across this work include Data Quality and Management (3 papers), Topic Modeling (3 papers), Semantic Web and Ontologies (2 papers), Data Stream Mining Techniques (1 paper), Bayesian Modeling and Causal Inference (1 paper), Genomics and Phylogenetic Studies (1 paper), Biomedical Text Mining and Ontologies (1 paper) and Imbalanced Data Classification Techniques (1 paper). The work is most often cited by research in Signal Processing (109 citations), Artificial Intelligence (152 citations), Computer Networks and Communications (98 citations), Management Science and Operations Research (41 citations) and Information Systems (47 citations). Chris Ré has collaborated with scholars based in United States and Lebanon. Frequent co-authors include Dan Suciu, Bhushan Mandhani, Nilesh Dalvi, Stephen H. Bach, Alexander Ratner, Henry R. Ehrenberg, Alex Ratner, Chong Luo, Braden Hancock and Daniel Rodriguez Gutierrez. Their work appears in journals such as BMC Bioinformatics, Communications of the ACM and PubMed.
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.