Émilie Devijver
Impact in
- Artificial Intelligence top 10%
- Bayesian Methods and Mixture Models
- Bayesian Modeling and Causal Inference
- Statistics and Probability top 10%
- Statistical Methods and Inference
Papers in
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- Bayesian Methods and Mixture Models 7
- Bayesian Modeling and Causal Inference 4
- Advanced Clustering Algorithms Research 3
- Machine Learning and Data Classification 3
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- Rough Sets and Fuzzy Logic 3
- Co-authors
- Éric Gaussier (4 shared papers)N. Jakse (7 shared papers)Massih-Reza Amini (2 shared papers)Roberta Poloni (3 shared papers)Yannig Goude (1 shared paper)Jean‐Michel Poggi (1 shared paper)João Paulo Almeida de Mendonça (2 shared papers)Jürgen Horbach (1 shared paper)
In The Last Decade
Émilie Devijver
20 papers receiving 214 citations
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 94
- Statistics and Probability 23
- Acoustics and Ultrasonics 2
- Signal Processing 20
- Materials Chemistry 72
Countries citing papers authored by Émilie Devijver
This map shows the geographic impact of Émilie Devijver'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 Émilie Devijver with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Émilie Devijver more than expected).
Fields of papers citing papers by Émilie Devijver
This network shows the impact of papers produced by Émilie Devijver. 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 Émilie Devijver. The network helps show where Émilie Devijver may publish in the future.
Co-authors
The 18 scholars most cited alongside Émilie Devijver, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 51 | |
| 2 | 2022 | 20 | |
| 3 | 2020 | 19 | |
| 4 | 2024 | 16 | |
| 5 | 2015 | 16 | |
| 6 | 2022 | 13 | |
| 7 | 2021 | 13 | |
| 8 | 2016 | 13 | |
| 9 | 2022 | 10 | |
| 10 | 2019 | 9 | |
| 11 | 2023 | 7 | |
| 12 | 2022 | 7 | |
| 13 | 2019 | 6 | |
| 14 | 2023 | 6 | |
| 15 | 2015 | 4 | |
| 16 | 2022 | 2 | |
| 17 | 2021 | 1 | |
| 18 | 2024 | 1 | |
| 19 | 2017 | 1 | |
| 20 | 2023 | 1 |
About Émilie Devijver
Émilie Devijver is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability, Materials Chemistry and Signal Processing, having authored 21 papers that have together received 216 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (7 papers), Statistical Methods and Inference (5 papers), Machine Learning in Materials Science (4 papers), Bayesian Modeling and Causal Inference (4 papers), Advanced Clustering Algorithms Research (3 papers), Rough Sets and Fuzzy Logic (3 papers), Machine Learning and Data Classification (3 papers) and Data Management and Algorithms (2 papers). The work is most often cited by research in Artificial Intelligence (94 citations), Statistics and Probability (23 citations), Acoustics and Ultrasonics (2 citations), Signal Processing (20 citations) and Materials Chemistry (72 citations). Émilie Devijver has collaborated with scholars based in France, Belgium and Germany. Frequent co-authors include Éric Gaussier, N. Jakse, Massih-Reza Amini, Roberta Poloni, Yannig Goude, Jean‐Michel Poggi, João Paulo Almeida de Mendonça, Jürgen Horbach, Philippe Jarry and Andreas Meyer. Their work appears in journals such as Electronic Journal of Statistics, Scientific Reports, Data Mining and Knowledge Discovery, Journal of the American Chemical Society and Journal of Multivariate Analysis.
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.