Matthew Fickus
- Applied Mathematics top 1%
- Computer Vision and Pattern Recognition top 5%
- Computational Mechanics top 5%
- Signal Processing top 5%
- Artificial Intelligence top 10%
- Co-authors
- John J. BenedettoDustin G. MixonPeter G. CasazzaJanet C. TremainJelena KovačevićYang WangGowri SrinivasaAfonso S. Bandeira
- Topics
- Mathematical Analysis and Transform Methods (28 papers)Image and Signal Denoising Methods (14 papers)Digital Filter Design and Implementation (12 papers)
- Journals
- IEEE Transactions on Information TheoryIEEE Transactions on Image ProcessingIEEE Transactions on Signal Processing
- Partner nations
- United StatesIndiaSwitzerland
In The Last Decade
Matthew Fickus
49 papers receiving 779 citations
Peers
Comparison fields: 5 of 79
- Applied Mathematics 412
- Computer Vision and Pattern Recognition 314
- Computational Mechanics 254
- Signal Processing 146
- Artificial Intelligence 142
Countries citing papers authored by Matthew Fickus
This map shows the geographic impact of Matthew Fickus'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 Matthew Fickus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Fickus more than expected).
Fields of papers citing papers by Matthew Fickus
This network shows the impact of papers produced by Matthew Fickus. 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 Matthew Fickus. The network helps show where Matthew Fickus may publish in the future.
Co-authorship network of co-authors of Matthew Fickus
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Fickus. A scholar is included among the top collaborators of Matthew Fickus 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 Matthew Fickus. Matthew Fickus is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | A generalized Schur-Horn theorem and optimal frame completions | 5 |
| 5 | 5 | |
| 6 | 15 | |
| 7 | 1 | |
| 8 | 36 | |
| 9 | 2 | |
| 10 | 13 | |
| 11 | 4 | |
| 12 | 17 | |
| 13 | 95 | |
| 14 | 1 | |
| 15 | 1 | |
| 16 | 39 | |
| 17 | 48 | |
| 18 | 3 | |
| 19 | 15 | |
| 20 | 2 |
About Matthew Fickus
Matthew Fickus is a scholar working on Applied Mathematics, Signal Processing and Computer Vision and Pattern Recognition, having authored 53 papers that have together received 824 indexed citations. Recurring topics across this work include Mathematical Analysis and Transform Methods (28 papers), Image and Signal Denoising Methods (14 papers) and Digital Filter Design and Implementation (12 papers). The work is most often cited by research in Applied Mathematics (412 citations), Computer Vision and Pattern Recognition (314 citations) and Signal Processing (146 citations). Matthew Fickus has collaborated with scholars based in United States, India and Switzerland. Frequent co-authors include John J. Benedetto, Dustin G. Mixon, Peter G. Casazza, Janet C. Tremain, Jelena Kovačević, Yang Wang, Gowri Srinivasa, Afonso S. Bandeira, John A. Ozolek and Yusong Guo. Their work appears in journals such as IEEE Transactions on Information Theory, IEEE Transactions on Image Processing and IEEE Transactions on Signal Processing.
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.