Geoffrey M. Davis
- Computer Vision and Pattern Recognition top 1%
- Computational Mechanics top 1%
- Pulmonary and Respiratory Medicine top 5%
- Physiology top 5%
- Signal Processing top 1%
- Co-authors
- Stéphane MallatM. AvellanedaFrancine M. DucharmeWim SweldensRoger L. ClaypooleRichard G. BaraniukDominic ChalutFrancisco Noya
- Topics
- Advanced Data Compression Techniques (16 papers)Image and Signal Denoising Methods (14 papers)Asthma and respiratory diseases (8 papers)
- Journals
- New England Journal of MedicineThe LancetAmerican Journal of Respiratory and Critical Care Medicine
- Partner nations
- United StatesCanadaAustralia
In The Last Decade
Geoffrey M. Davis
40 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Computer Vision and Pattern Recognition 880
- Computational Mechanics 650
- Pulmonary and Respiratory Medicine 617
- Physiology 504
- Signal Processing 476
Countries citing papers authored by Geoffrey M. Davis
This map shows the geographic impact of Geoffrey M. Davis'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 Geoffrey M. Davis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Geoffrey M. Davis more than expected).
Fields of papers citing papers by Geoffrey M. Davis
This network shows the impact of papers produced by Geoffrey M. Davis. 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 Geoffrey M. Davis. The network helps show where Geoffrey M. Davis may publish in the future.
Co-authorship network of co-authors of Geoffrey M. Davis
This figure shows the co-authorship network connecting the top 25 collaborators of Geoffrey M. Davis. A scholar is included among the top collaborators of Geoffrey M. Davis 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 Geoffrey M. Davis. Geoffrey M. Davis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 13 | |
| 3 | 202 | |
| 4 | 95 | |
| 5 | 33 | |
| 6 | 195 | |
| 7 | 6 | |
| 8 | 114 | |
| 9 | 46 | |
| 10 | 51 | |
| 11 | 127 | |
| 12 | 52 | |
| 13 | 3 | |
| 14 | Adaptive Wavelet Transforms for Image Coding | 5 |
| 15 | 66 | |
| 16 | Adaptive greedy approximationsbreakdown → | 768 |
| 17 | 16 | |
| 18 | 30 | |
| 19 | 28 | |
| 20 | 2 |
About Geoffrey M. Davis
Geoffrey M. Davis is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Mathematical Physics, having authored 41 papers that have together received 2.4k indexed citations. Recurring topics across this work include Advanced Data Compression Techniques (16 papers), Image and Signal Denoising Methods (14 papers) and Asthma and respiratory diseases (8 papers). The work is most often cited by research in Signal Processing (476 citations), Computer Vision and Pattern Recognition (880 citations) and Computational Mechanics (650 citations). Geoffrey M. Davis has collaborated with scholars based in United States, Canada and Australia. Frequent co-authors include Stéphane Mallat, M. Avellaneda, Francine M. Ducharme, Wim Sweldens, Roger L. Claypoole, Richard G. Baraniuk, Dominic Chalut, Francisco Noya, John M. Danskin and Robert W. Platt. Their work appears in journals such as New England Journal of Medicine, The Lancet and American Journal of Respiratory and Critical Care Medicine.
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.