Joël Grus
Impact in
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- Topic Modeling
- Natural Language Processing Techniques
- Anomaly Detection Techniques and Applications
- Advanced Graph Neural Networks
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- Time Series Analysis and Forecasting
Papers in
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- Natural Language Processing Techniques 2
- Topic Modeling 2
- Computational Physics and Python Applications 1
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- Biomedical Text Mining and Ontologies 1
- Co-authors
- Wen-tau Yih (1 shared paper)Peter E. Clark (1 shared paper)Niket Tandon (1 shared paper)Antoine Bosselut (1 shared paper)Bhavana Dalvi (1 shared paper)Matt Gardner (1 shared paper)Mark E Neumann (1 shared paper)Nicholas Lourie (1 shared paper)
- Journals
- Empirical Methods in Natural Language Processing (1 paper)CERN Document Server (European Organization for Nuclear Research) (2 papers)
- Partner nations
- United States
In The Last Decade
Joël Grus
4 papers receiving 164 citations
Peers
Comparison fields: 5 of 89
- Artificial Intelligence 76
- Signal Processing 13
- Management Information Systems 10
- Computer Vision and Pattern Recognition 18
- Anatomy 1
Countries citing papers authored by Joël Grus
This map shows the geographic impact of Joël Grus'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 Joël Grus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joël Grus more than expected).
Fields of papers citing papers by Joël Grus
This network shows the impact of papers produced by Joël Grus. 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 Joël Grus. The network helps show where Joël Grus may publish in the future.
Co-authors
The 8 scholars most cited alongside Joël Grus, 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 | Data Science from Scratch: First Principles with Python | 2015 | 107 |
| 2 | 2018 | 39 | |
| 3 | Data science from scratch | 2015 | 25 |
| 4 | Writing Code for NLP Research | 2018 | 2 |
| 5 | Data science do zero | 2016 | 0 |
About Joël Grus
Joël Grus is a scholar working on Artificial Intelligence, Molecular Biology, Infectious Diseases, Organic Chemistry and Surgery, having authored 5 papers that have together received 173 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (2 papers), Topic Modeling (2 papers), Biomedical Text Mining and Ontologies (1 paper) and Computational Physics and Python Applications (1 paper). The work is most often cited by research in Artificial Intelligence (76 citations), Signal Processing (13 citations), Management Information Systems (10 citations), Computer Vision and Pattern Recognition (18 citations) and Anatomy (1 citation). Joël Grus has collaborated with scholars based in United States. Frequent co-authors include Wen-tau Yih, Peter E. Clark, Niket Tandon, Antoine Bosselut, Bhavana Dalvi, Matt Gardner, Mark E Neumann and Nicholas Lourie. Their work appears in journals such as Empirical Methods in Natural Language Processing and CERN Document Server (European Organization for Nuclear Research).
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.