Chris Ré
Impact in
- Signal Processing top 5%
- Data Management and Algorithms
- Artificial Intelligence top 10%
- Semantic Web and Ontologies
- Topic Modeling
- Natural Language Processing Techniques
- Bayesian Modeling and Causal Inference
- Machine Learning and Data Classification
Papers in
-
- Topic Modeling 2
- Semantic Web and Ontologies 2
- Imbalanced Data Classification Techniques 1
- Advanced Graph Neural Networks 1
- Bayesian Modeling and Causal Inference 1
-
- Data Quality and Management 3
- Co-authors
- Bhushan Mandhani (1 shared paper)Dan Suciu (1 shared paper)Nilesh Dalvi (1 shared paper)Stephen H. Bach (2 shared papers)Henry R. Ehrenberg (1 shared paper)Alexander Ratner (1 shared paper)Alex Ratner (3 shared papers)Souvik Sen (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 254 citations
Peers
Comparison fields: 5 of 45
- Signal Processing 129
- Artificial Intelligence 175
- Computer Networks and Communications 119
- Management Science and Operations Research 50
- Information Systems 51
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 23 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 | 153 | |
| 2 | 2019 | 50 | |
| 3 | 2017 | 46 | |
| 4 | 2020 | 15 | |
| 5 | Creating Robust Relation Extract and Anomaly Detect via Probabilistic Logic-Based Reasoning and Learning | 2017 | 2 |
| 6 | 2024 | 2 | |
| 7 | 2018 | 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 269 indexed citations. Recurring topics across this work include Data Quality and Management (3 papers), Topic Modeling (2 papers), Semantic Web and Ontologies (2 papers), Imbalanced Data Classification Techniques (1 paper), Software System Performance and Reliability (1 paper), Biomedical Text Mining and Ontologies (1 paper), Advanced Graph Neural Networks (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Signal Processing (129 citations), Artificial Intelligence (175 citations), Computer Networks and Communications (119 citations), Management Science and Operations Research (50 citations) and Information Systems (51 citations). Chris Ré has collaborated with scholars based in United States and Lebanon. Frequent co-authors include Bhushan Mandhani, Dan Suciu, Nilesh Dalvi, Stephen H. Bach, Henry R. Ehrenberg, Alexander Ratner, Alex Ratner, Souvik Sen, Daniel Rodriguez Gutierrez and Haidong Shao. 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.