Francis Bach
- Computer Vision and Pattern Recognition top 5%
- Rheumatology top 10%
- Public Health, Environmental and Occupational Health
- Artificial Intelligence
- Immunology
- Co-authors
- Jean PonceA KAYA. FreedmanA. St. J. DixonRodolphe JenattonJulien MairalAlexandre d’AspremontJean‐Philippe Vert
- Topics
- Sparse and Compressive Sensing Techniques (5 papers)Statistical Methods and Inference (2 papers)Renal Diseases and Glomerulopathies (2 papers)
In The Last Decade
Francis Bach
17 papers receiving 399 citations
Peers
Comparison fields: 5 of 94
- Computer Vision and Pattern Recognition 200
- Rheumatology 113
- Public Health, Environmental and Occupational Health 47
- Artificial Intelligence 41
- Immunology 39
Countries citing papers authored by Francis Bach
This map shows the geographic impact of Francis Bach'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 Francis Bach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francis Bach more than expected).
Fields of papers citing papers by Francis Bach
This network shows the impact of papers produced by Francis Bach. 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 Francis Bach. The network helps show where Francis Bach may publish in the future.
Co-authorship network of co-authors of Francis Bach
This figure shows the co-authorship network connecting the top 25 collaborators of Francis Bach. A scholar is included among the top collaborators of Francis Bach 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 Francis Bach. Francis Bach is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 1 | |
| 6 | 27 | |
| 7 | Optimization with Sparsity-Inducing Penalties (Foundations and Trends(R) in Machine Learning) | 17 |
| 8 | Other Grants and Activities - European Research Council (ERC)Starting Investigator Researcher grant | 1 |
| 9 | 2 | |
| 10 | Assessment of a diagnostic dip-stick for assaying plasma or serum pseudocholinesterase activity. | 2 |
| 11 | 3 | |
| 12 | 79 | |
| 13 | 45 | |
| 14 | 1 | |
| 15 | 1 | |
| 16 | 0 | |
| 17 | 1 | |
| 18 | 32 | |
| 19 | 2 | |
| 20 | 9 |
About Francis Bach
Francis Bach is a scholar working on Numerical Analysis, Statistics and Probability and Nephrology, having authored 22 papers that have together received 439 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (5 papers), Statistical Methods and Inference (2 papers) and Renal Diseases and Glomerulopathies (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (200 citations), Rheumatology (113 citations) and Media Technology (23 citations). Francis Bach has collaborated with scholars based in France, India and Burundi. Frequent co-authors include Jean Ponce, A KAY, A. Freedman, A. St. J. Dixon, Rodolphe Jenatton, Julien Mairal, Alexandre d’Aspremont, Jean‐Philippe Vert, Gudrun Schleiermacher and Julie Cappo. Their work appears in journals such as The Lancet, Annals of the Rheumatic Diseases and BMC Bioinformatics.
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.