Margaret E. Lundy
- Computational Mathematics top 0.2%
- Artificial Intelligence top 5%
- Industrial and Manufacturing Engineering top 2%
- Signal Processing top 5%
- Computational Theory and Mathematics top 5%
- Co-authors
- Richard A. HarshmanA.I. MeesJoseph B. KruskalRasmus BroNicholas D. SidiropoulosPaul E. GreenYoram WindSungjin Hong
- Topics
- Tensor decomposition and applications (4 papers)Blind Source Separation Techniques (4 papers)Metaheuristic Optimization Algorithms Research (2 papers)
- Partner nations
- CanadaUnited KingdomAustralia
In The Last Decade
Margaret E. Lundy
12 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 144
- Computational Mathematics 296
- Artificial Intelligence 261
- Industrial and Manufacturing Engineering 238
- Signal Processing 233
- Computational Theory and Mathematics 156
Countries citing papers authored by Margaret E. Lundy
This map shows the geographic impact of Margaret E. Lundy'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 Margaret E. Lundy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Margaret E. Lundy more than expected).
Fields of papers citing papers by Margaret E. Lundy
This network shows the impact of papers produced by Margaret E. Lundy. 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 Margaret E. Lundy. The network helps show where Margaret E. Lundy may publish in the future.
Co-authorship network of co-authors of Margaret E. Lundy
This figure shows the co-authorship network connecting the top 25 collaborators of Margaret E. Lundy. A scholar is included among the top collaborators of Margaret E. Lundy 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 Margaret E. Lundy. Margaret E. Lundy 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 | 81 | |
| 3 | 44 | |
| 4 | 23 | |
| 5 | 16 | |
| 6 | 79 | |
| 7 | 371 | |
| 8 | A two-stage procedure incorporating good features of both trilinear and quadrilinear models | 20 |
| 9 | How 3-MFA data can cause degenerate parafac solutions, among other relationships | 59 |
| 10 | 498 | |
| 11 | 61 | |
| 12 | 77 | |
| 13 | 1 |
About Margaret E. Lundy
Margaret E. Lundy is a scholar working on Computational Mathematics, Signal Processing and Fluid Flow and Transfer Processes, having authored 13 papers that have together received 1.3k indexed citations. Recurring topics across this work include Tensor decomposition and applications (4 papers), Blind Source Separation Techniques (4 papers) and Metaheuristic Optimization Algorithms Research (2 papers). The work is most often cited by research in Computational Mathematics (296 citations), Industrial and Manufacturing Engineering (238 citations) and Signal Processing (233 citations). Margaret E. Lundy has collaborated with scholars based in Canada, United Kingdom and Australia. Frequent co-authors include Richard A. Harshman, A.I. Mees, Joseph B. Kruskal, Rasmus Bro, Nicholas D. Sidiropoulos, Paul E. Green, Yoram Wind, Sungjin Hong, Jacquelyn Burkell and Leora C. Swartzman. Their work appears in journals such as The Journal of the Acoustical Society of America, Biometrika and Marketing Science.
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.