Émilie Devijver

441 total citations
21 papers, 216 citations indexed

About

Émilie Devijver is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Statistics and Probability. According to data from OpenAlex, Émilie Devijver has authored 21 papers receiving a total of 216 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 5 papers in Computational Theory and Mathematics and 5 papers in Statistics and Probability. Recurrent topics in Émilie Devijver's work include Bayesian Methods and Mixture Models (7 papers), Statistical Methods and Inference (5 papers) and Bayesian Modeling and Causal Inference (4 papers). Émilie Devijver is often cited by papers focused on Bayesian Methods and Mixture Models (7 papers), Statistical Methods and Inference (5 papers) and Bayesian Modeling and Causal Inference (4 papers). Émilie Devijver collaborates with scholars based in France, Belgium and Germany. Émilie Devijver's co-authors include Éric Gaussier, N. Jakse, Massih-Reza Amini, Roberta Poloni, Jean‐Michel Poggi, Yannig Goude, João Paulo Almeida de Mendonça, Andreas Meyer, Philippe Jarry and Jürgen Horbach and has published in prestigious journals such as Journal of the American Chemical Society, Scientific Reports and Journal of Chemical Theory and Computation.

In The Last Decade

Émilie Devijver

20 papers receiving 214 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Émilie Devijver France 10 94 72 23 21 20 21 216
Amos Waterland United States 9 52 0.6× 66 0.9× 8 0.3× 15 0.7× 11 0.6× 17 350
Kanta Naito Japan 10 52 0.6× 30 0.4× 131 5.7× 4 0.2× 10 0.5× 51 278
Danuta Rutkowska Poland 10 106 1.1× 5 0.1× 11 0.5× 21 1.0× 17 0.8× 27 302
Khoa Lê United Kingdom 9 9 0.1× 20 0.3× 10 0.4× 35 1.7× 20 1.0× 21 208
A. Ustyuzhanin Russia 7 39 0.4× 61 0.8× 2 0.1× 6 0.3× 8 0.4× 46 186
Chaim Goodman-Strauss United States 9 18 0.2× 104 1.4× 12 0.5× 109 5.2× 4 0.2× 16 220
Stephan Thaler Germany 7 21 0.2× 76 1.1× 3 0.1× 36 1.7× 7 0.3× 13 167
Pierre Calka France 9 32 0.3× 14 0.2× 42 1.8× 8 0.4× 11 0.6× 27 212
Guillaume Verdon Canada 4 340 3.6× 37 0.5× 4 0.2× 61 2.9× 6 0.3× 5 411
Bogdan Burlacu Austria 8 173 1.8× 16 0.2× 3 0.1× 45 2.1× 10 0.5× 23 258

Countries citing papers authored by Émilie Devijver

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Émilie Devijver

This figure shows the co-authorship network connecting the top 25 collaborators of Émilie Devijver. A scholar is included among the top collaborators of Émilie Devijver based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Émilie Devijver. Émilie Devijver is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Brault, Vincent, et al.. (2024). Mixture of segmentation for heterogeneous functional data. Electronic Journal of Statistics. 18(2). 1 indexed citations
2.
Devijver, Émilie, et al.. (2024). Informative Training Data for Efficient Property Prediction in Metal–Organic Frameworks by Active Learning. Journal of the American Chemical Society. 146(9). 6134–6144. 16 indexed citations
3.
Mendonça, João Paulo Almeida de, et al.. (2023). Regression tree-based active learning. Data Mining and Knowledge Discovery. 38(2). 420–460. 7 indexed citations
4.
Devijver, Émilie, et al.. (2023). Survey and Evaluation of Causal Discovery Methods for Time Series (Extended Abstract). SPIRE - Sciences Po Institutional REpository. 6839–6844. 1 indexed citations
5.
Mendonça, João Paulo Almeida de, et al.. (2023). Artificial Neural Network-Based Density Functional Approach for Adiabatic Energy Differences in Transition Metal Complexes. Journal of Chemical Theory and Computation. 19(21). 7555–7566. 6 indexed citations
6.
Jakse, N., Philippe Jarry, Émilie Devijver, et al.. (2022). Machine learning interatomic potentials for aluminium: application to solidification phenomena. Journal of Physics Condensed Matter. 35(3). 35402–35402. 13 indexed citations
7.
Devijver, Émilie, et al.. (2022). Unsupervised topological learning approach of crystal nucleation. Scientific Reports. 12(1). 10 indexed citations
8.
Devijver, Émilie, et al.. (2022). Entropy-Based Discovery of Summary Causal Graphs in Time Series. Entropy. 24(8). 1156–1156. 7 indexed citations
9.
Devijver, Émilie, et al.. (2022). A Conditional Mutual Information Estimator for Mixed Data and an Associated Conditional Independence Test. Entropy. 24(9). 1234–1234. 2 indexed citations
10.
Devijver, Émilie, et al.. (2022). Wrapper feature selection with partially labeled data. Applied Intelligence. 52(11). 12316–12329. 20 indexed citations
11.
Devijver, Émilie, et al.. (2022). Survey and Evaluation of Causal Discovery Methods for Time Series. Journal of Artificial Intelligence Research. 73. 767–819. 51 indexed citations
12.
Claeskens, Gerda, Émilie Devijver, & Irène Gijbels. (2021). Nonlinear mixed effects modeling and warping for functional data using B-splines. Electronic Journal of Statistics. 15(2). 1 indexed citations
13.
Devijver, Émilie, et al.. (2021). Unsupervised topological learning for identification of atomic structures. arXiv (Cornell University). 13 indexed citations
14.
Devijver, Émilie, et al.. (2020). Glass-forming ability of elemental zirconium. Physical review. B.. 102(10). 19 indexed citations
15.
Devijver, Émilie, Yannig Goude, & Jean‐Michel Poggi. (2019). Clustering electricity consumers using high‐dimensional regression mixture models. Applied Stochastic Models in Business and Industry. 36(1). 159–177. 9 indexed citations
16.
Devijver, Émilie, et al.. (2019). Transductive Bounds for the Multi-Class Majority Vote Classifier. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 3566–3573. 6 indexed citations
17.
Devijver, Émilie. (2017). Joint rank and variable selection for parsimonious estimation in a high-dimensional finite mixture regression model. Journal of Multivariate Analysis. 157. 1–13. 1 indexed citations
18.
Devijver, Émilie. (2016). Model-based regression clustering for high-dimensional data: application to functional data. Advances in Data Analysis and Classification. 11(2). 243–279. 13 indexed citations
19.
Devijver, Émilie. (2015). Finite mixture regression: A sparse variable selection by model selection for clustering. Electronic Journal of Statistics. 9(2). 16 indexed citations
20.
Devijver, Émilie. (2015). Anℓ1-oracle inequality for the Lasso in multivariate finite mixture of multivariate Gaussian regression models. ESAIM Probability and Statistics. 19. 649–670. 4 indexed citations

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.

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