Rahul Shah
- Artificial Intelligence
- Signal Processing top 10%
- Computer Networks and Communications
- Computational Theory and Mathematics
- Information Systems
- Co-authors
- Sunil PrabhakarChris MayfieldSarvjeet SinghSusanne E. HambruschSharma V. ThankachanShonali KrishnaswamyMohamed Medhat GaberArnab Ganguly
- Topics
- Algorithms and Data Compression (12 papers)Network Packet Processing and Optimization (6 papers)DNA and Biological Computing (5 papers)
- Partner nations
- United StatesCanadaChile
In The Last Decade
Rahul Shah
16 papers receiving 96 citations
Peers
Comparison fields: 5 of 19
- Artificial Intelligence 74
- Signal Processing 64
- Computer Networks and Communications 51
- Computational Theory and Mathematics 15
- Information Systems 14
Countries citing papers authored by Rahul Shah
This map shows the geographic impact of Rahul Shah'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 Rahul Shah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rahul Shah more than expected).
Fields of papers citing papers by Rahul Shah
This network shows the impact of papers produced by Rahul Shah. 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 Rahul Shah. The network helps show where Rahul Shah may publish in the future.
Co-authorship network of co-authors of Rahul Shah
This figure shows the co-authorship network connecting the top 25 collaborators of Rahul Shah. A scholar is included among the top collaborators of Rahul Shah 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 Rahul Shah. Rahul Shah is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 3 | |
| 7 | 2 | |
| 8 | 4 | |
| 9 | 2 | |
| 10 | 2 | |
| 11 | 1 | |
| 12 | 6 | |
| 13 | 5 | |
| 14 | 0 | |
| 15 | 58 | |
| 16 | 2 | |
| 17 | Resource-aware Very Fast K-Means for ubiquitous data stream mining | 14 |
| 18 | 0 | |
| 19 | Mobile Filters for Efficient Dissemination of Personalized Information Using Content-Based Multicast | 3 |
About Rahul Shah
Rahul Shah is a scholar working on Hardware and Architecture, Computational Theory and Mathematics and Artificial Intelligence, having authored 19 papers that have together received 108 indexed citations. Recurring topics across this work include Algorithms and Data Compression (12 papers), Network Packet Processing and Optimization (6 papers) and DNA and Biological Computing (5 papers). The work is most often cited by research in Signal Processing (64 citations), Artificial Intelligence (74 citations) and Computer Networks and Communications (51 citations). Rahul Shah has collaborated with scholars based in United States, Canada and Chile. Frequent co-authors include Sunil Prabhakar, Chris Mayfield, Sarvjeet Singh, Susanne E. Hambrusch, Sharma V. Thankachan, Shonali Krishnaswamy, Mohamed Medhat Gaber, Arnab Ganguly, Martı́n Farach-Colton and Gonzalo Navarro. Their work appears in journals such as Theoretical Computer Science, Algorithmica and Journal of Classification.
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.