Anna Seigal
Impact in
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- Tensor decomposition and applications
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- Commutative Algebra and Its Applications
Papers in
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- Tensor decomposition and applications 4
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- Topological and Geometric Data Analysis 2
- Matrix Theory and Algorithms 1
- Polynomial and algebraic computation 1
- Co-authors
- Max J. Pfeffer (1 shared paper)Bernd Sturmfels (1 shared paper)Vidit Nanda (1 shared paper)Heather A. Harrington (1 shared paper)Sergeî Yakovenko (1 shared paper)Kexin Wang (1 shared paper)Terry Lyons (1 shared paper)Peter K. Friz (1 shared paper)
- Journals
- Electronic Journal of Statistics (1 paper)Israel Journal of Mathematics (1 paper)Foundations of Computational Mathematics (1 paper)Bulletin of the London Mathematical Society (1 paper)SIAM Journal on Matrix Analysis and Applications (1 paper)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Anna Seigal
7 papers receiving 22 citations
Peers
Comparison fields: 5 of 21
- Computational Mathematics 5
- Algebra and Number Theory 5
- Geometry and Topology 8
- Mathematical Physics 6
- Discrete Mathematics and Combinatorics 2
Countries citing papers authored by Anna Seigal
This map shows the geographic impact of Anna Seigal'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 Anna Seigal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anna Seigal more than expected).
Fields of papers citing papers by Anna Seigal
This network shows the impact of papers produced by Anna Seigal. 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 Anna Seigal. The network helps show where Anna Seigal may publish in the future.
Co-authors
The 8 scholars most cited alongside Anna Seigal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 11 | |
| 2 | 2022 | 5 | |
| 3 | 2021 | 3 | |
| 4 | 2014 | 3 | |
| 5 | 2023 | 1 | |
| 6 | 2023 | 1 | |
| 7 | Structured Tensors and the Geometry of Data | 2019 | 1 |
| 8 | 2024 | 0 | |
| 9 | 2024 | 0 |
About Anna Seigal
Anna Seigal is a scholar working on Computational Mathematics, Computational Theory and Mathematics, Artificial Intelligence, Statistics and Probability and Geometry and Topology, having authored 9 papers that have together received 25 indexed citations. Recurring topics across this work include Tensor decomposition and applications (4 papers), Markov Chains and Monte Carlo Methods (3 papers), Bayesian Modeling and Causal Inference (2 papers), Topological and Geometric Data Analysis (2 papers), Sparse and Compressive Sensing Techniques (2 papers), Data Management and Algorithms (1 paper), Matrix Theory and Algorithms (1 paper) and Polynomial and algebraic computation (1 paper). The work is most often cited by research in Computational Mathematics (5 citations), Algebra and Number Theory (5 citations), Geometry and Topology (8 citations), Mathematical Physics (6 citations) and Discrete Mathematics and Combinatorics (2 citations). Anna Seigal has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Max J. Pfeffer, Bernd Sturmfels, Vidit Nanda, Heather A. Harrington, Sergeî Yakovenko, Kexin Wang, Terry Lyons and Peter K. Friz. Their work appears in journals such as Electronic Journal of Statistics, Israel Journal of Mathematics, Foundations of Computational Mathematics, Bulletin of the London Mathematical Society and SIAM Journal on Matrix Analysis and Applications.
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.