Nikhil Rao
- Surgery top 10%
- Artificial Intelligence top 5%
- Biomaterials top 5%
- Biomedical Engineering top 10%
- Computational Mechanics top 5%
- Co-authors
- Inderjit S. DhillonHsiang‐Fu YuKaren L. ChristmanRobert D. NowakJessica L. UngerleiderGregory N. GroverPradeep RavikumarTodd D. Johnson
- Topics
- Sparse and Compressive Sensing Techniques (13 papers)Advanced Graph Neural Networks (8 papers)Topic Modeling (6 papers)
- Journals
- ACS NanoBiomaterialsGut
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Nikhil Rao
59 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 134
- Surgery 337
- Artificial Intelligence 314
- Biomaterials 271
- Biomedical Engineering 262
- Computational Mechanics 179
Countries citing papers authored by Nikhil Rao
This map shows the geographic impact of Nikhil Rao'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 Nikhil Rao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nikhil Rao more than expected).
Fields of papers citing papers by Nikhil Rao
This network shows the impact of papers produced by Nikhil Rao. 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 Nikhil Rao. The network helps show where Nikhil Rao may publish in the future.
Co-authorship network of co-authors of Nikhil Rao
This figure shows the co-authorship network connecting the top 25 collaborators of Nikhil Rao. A scholar is included among the top collaborators of Nikhil Rao 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 Nikhil Rao. Nikhil Rao 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 | 2 | |
| 4 | 7 | |
| 5 | 5 | |
| 6 | 2 | |
| 7 | 5 | |
| 8 | 12 | |
| 9 | 25 | |
| 10 | 9 | |
| 11 | Discovery of Evolving Semantics through Dynamic Word Embedding Learning. | 3 |
| 12 | 60 | |
| 13 | Temporal regularized matrix factorization for high-dimensional time series prediction | 215 |
| 14 | Sparse and low-rank tensor decomposition | 9 |
| 15 | Temporal Regularized Matrix Factorization. | 3 |
| 16 | Collaborative filtering with graph information: consistency and scalable methods | 118 |
| 17 | 85 | |
| 18 | Universal Measurement Bounds for Structured Sparse Signal Recovery | 23 |
| 19 | 1 | |
| 20 | 66 |
About Nikhil Rao
Nikhil Rao is a scholar working on Computational Mathematics, Computational Mechanics and Artificial Intelligence, having authored 63 papers that have together received 1.3k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (13 papers), Advanced Graph Neural Networks (8 papers) and Topic Modeling (6 papers). The work is most often cited by research in Computational Mathematics (48 citations), Biomaterials (271 citations) and Signal Processing (143 citations). Nikhil Rao has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Inderjit S. Dhillon, Hsiang‐Fu Yu, Karen L. Christman, Robert D. Nowak, Jessica L. Ungerleider, Gregory N. Grover, Pradeep Ravikumar, Todd D. Johnson, Stephen J. Wright and Timothy T. Rogers. Their work appears in journals such as ACS Nano, Biomaterials and Gut.
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.