Joseph D. Lakey
- Applied Mathematics top 2%
- Computer Vision and Pattern Recognition top 10%
- Mathematical Physics top 10%
- Signal Processing top 10%
- Computational Mechanics
- Co-authors
- Jeffrey A. HoganJosefina ÁlvarezMaría Cristina PereyraSam EfromovichGeorge H. WeissJohn J. BenedettoWade TrappeJames K. Kroger
- Topics
- Mathematical Analysis and Transform Methods (25 papers)Image and Signal Denoising Methods (13 papers)Seismic Imaging and Inversion Techniques (5 papers)
- Journals
- IEEE Transactions on Signal ProcessingThe Journal of the Acoustical Society of AmericaJournal of Computational and Applied Mathematics
- Partner nations
- United StatesAustraliaVietnam
In The Last Decade
Joseph D. Lakey
29 papers receiving 279 citations
Peers
Comparison fields: 5 of 46
- Applied Mathematics 220
- Computer Vision and Pattern Recognition 107
- Mathematical Physics 74
- Signal Processing 46
- Computational Mechanics 38
Countries citing papers authored by Joseph D. Lakey
This map shows the geographic impact of Joseph D. Lakey'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 Joseph D. Lakey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joseph D. Lakey more than expected).
Fields of papers citing papers by Joseph D. Lakey
This network shows the impact of papers produced by Joseph D. Lakey. 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 Joseph D. Lakey. The network helps show where Joseph D. Lakey may publish in the future.
Co-authorship network of co-authors of Joseph D. Lakey
This figure shows the co-authorship network connecting the top 25 collaborators of Joseph D. Lakey. A scholar is included among the top collaborators of Joseph D. Lakey 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 Joseph D. Lakey. Joseph D. Lakey 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 | 1 | |
| 3 | 0 | |
| 4 | 10 | |
| 5 | 35 | |
| 6 | 3 | |
| 7 | 6 | |
| 8 | 4 | |
| 9 | 3 | |
| 10 | 4 | |
| 11 | 3 | |
| 12 | 24 | |
| 13 | 4 | |
| 14 | Spaces of bounded $\lambda$-central mean oscillation, Morrey spaces, and $\lambda$-central Carleson measures | 53 |
| 15 | 1 | |
| 16 | 14 | |
| 17 | 7 | |
| 18 | 4 | |
| 19 | 1 | |
| 20 | 0 |
About Joseph D. Lakey
Joseph D. Lakey is a scholar working on Applied Mathematics, Mathematical Physics and Acoustics and Ultrasonics, having authored 36 papers that have together received 295 indexed citations. Recurring topics across this work include Mathematical Analysis and Transform Methods (25 papers), Image and Signal Denoising Methods (13 papers) and Seismic Imaging and Inversion Techniques (5 papers). The work is most often cited by research in Applied Mathematics (220 citations), Mathematical Physics (74 citations) and Computer Vision and Pattern Recognition (107 citations). Joseph D. Lakey has collaborated with scholars based in United States, Australia and Vietnam. Frequent co-authors include Jeffrey A. Hogan, Josefina Álvarez, María Cristina Pereyra, Sam Efromovich, George H. Weiss, John J. Benedetto, Wade Trappe, James K. Kroger and John E. Gilbert. Their work appears in journals such as IEEE Transactions on Signal Processing, The Journal of the Acoustical Society of America and Journal of Computational and Applied Mathematics.
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.