Florence Forbes
- Artificial Intelligence top 2%
- Genetics top 5%
- Computer Vision and Pattern Recognition top 2%
- Statistics and Probability top 0.5%
- Ecology top 5%
- Co-authors
- Gilles CeleuxOlivier FrançoisD. M. TitteringtonCaroline RobertChibiao ChenÉric DurandNathalie PeyrardMichel Dojat
- Topics
- Bayesian Methods and Mixture Models (34 papers)Statistical Methods and Inference (19 papers)Medical Image Segmentation Techniques (11 papers)
- Journals
- Journal of the American Statistical AssociationBioinformaticsIEEE Transactions on Pattern Analysis and Machine Intelligence
- Partner nations
- FranceAustraliaUnited States
In The Last Decade
Florence Forbes
87 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 173
- Artificial Intelligence 782
- Genetics 568
- Computer Vision and Pattern Recognition 545
- Statistics and Probability 538
- Ecology 345
Countries citing papers authored by Florence Forbes
This map shows the geographic impact of Florence Forbes'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 Florence Forbes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Florence Forbes more than expected).
Fields of papers citing papers by Florence Forbes
This network shows the impact of papers produced by Florence Forbes. 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 Florence Forbes. The network helps show where Florence Forbes may publish in the future.
Co-authorship network of co-authors of Florence Forbes
This figure shows the co-authorship network connecting the top 25 collaborators of Florence Forbes. A scholar is included among the top collaborators of Florence Forbes 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 Florence Forbes. Florence Forbes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 63 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 12 | |
| 6 | 0 | |
| 7 | 10 | |
| 8 | 3 | |
| 9 | 1 | |
| 10 | 3 | |
| 11 | 1 | |
| 12 | 62 | |
| 13 | 21 | |
| 14 | 9 | |
| 15 | 12 | |
| 16 | 52 | |
| 17 | 11 | |
| 18 | 1 | |
| 19 | 7 | |
| 20 | 3 |
About Florence Forbes
Florence Forbes is a scholar working on Statistics and Probability, Health Informatics and Artificial Intelligence, having authored 93 papers that have together received 2.7k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (34 papers), Statistical Methods and Inference (19 papers) and Medical Image Segmentation Techniques (11 papers). The work is most often cited by research in Statistics and Probability (538 citations), Computer Vision and Pattern Recognition (545 citations) and Ecological Modeling (109 citations). Florence Forbes has collaborated with scholars based in France, Australia and United States. Frequent co-authors include Gilles Celeux, Olivier François, D. M. Titterington, Caroline Robert, Chibiao Chen, Éric Durand, Nathalie Peyrard, Michel Dojat, Radu Horaud and Darren Wraith. Their work appears in journals such as Journal of the American Statistical Association, Bioinformatics and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.