Shashanka Ubaru
- Artificial Intelligence
- Computational Theory and Mathematics top 10%
- Computational Mechanics
- Signal Processing
- Statistical and Nonlinear Physics
- Co-authors
- Yousef SaadAbd‐Krim SeghouaneJames R. ChelikowskyLior HoreshArya MazumdarG. M. CohenKristofer E. BouchardKesheng Wu
- Topics
- Sparse and Compressive Sensing Techniques (7 papers)Matrix Theory and Algorithms (6 papers)Blind Source Separation Techniques (4 papers)
- Partner nations
- United StatesAustraliaSouth Africa
In The Last Decade
Shashanka Ubaru
18 papers receiving 152 citations
Peers
Comparison fields: 5 of 67
- Artificial Intelligence 49
- Computational Theory and Mathematics 40
- Computational Mechanics 37
- Signal Processing 24
- Statistical and Nonlinear Physics 22
Countries citing papers authored by Shashanka Ubaru
This map shows the geographic impact of Shashanka Ubaru'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 Shashanka Ubaru with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shashanka Ubaru more than expected).
Fields of papers citing papers by Shashanka Ubaru
This network shows the impact of papers produced by Shashanka Ubaru. 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 Shashanka Ubaru. The network helps show where Shashanka Ubaru may publish in the future.
Co-authorship network of co-authors of Shashanka Ubaru
This figure shows the co-authorship network connecting the top 25 collaborators of Shashanka Ubaru. A scholar is included among the top collaborators of Shashanka Ubaru 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 Shashanka Ubaru. Shashanka Ubaru 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 | 0 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 2 | |
| 8 | 3 | |
| 9 | 1 | |
| 10 | Projection techniques to update the truncated SVD of evolving matrices with applications | 2 |
| 11 | 6 | |
| 12 | 1 | |
| 13 | 2 | |
| 14 | Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs | 2 |
| 15 | Multilabel Classification with Group Testing and Codes | 6 |
| 16 | 7 | |
| 17 | 10 | |
| 18 | 26 | |
| 19 | Fast methods for estimating the numerical rank of large matrices | 9 |
| 20 | Low Rank Approximation using Error Correcting Coding Matrices | 4 |
About Shashanka Ubaru
Shashanka Ubaru is a scholar working on Computational Mathematics, Computational Theory and Mathematics and Artificial Intelligence, having authored 25 papers that have together received 163 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (7 papers), Matrix Theory and Algorithms (6 papers) and Blind Source Separation Techniques (4 papers). The work is most often cited by research in Computational Mathematics (9 citations), Acoustics and Ultrasonics (3 citations) and Computational Theory and Mathematics (40 citations). Shashanka Ubaru has collaborated with scholars based in United States, Australia and South Africa. Frequent co-authors include Yousef Saad, Abd‐Krim Seghouane, James R. Chelikowsky, Lior Horesh, Arya Mazumdar, G. M. Cohen, Kristofer E. Bouchard, Kesheng Wu, William Kirby and Theodoros Salonidis. Their work appears in journals such as Neural Computation, Physical review. B. and SIAM Journal on Scientific Computing.
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.