Nishkam Ravi
- Computer Vision and Pattern Recognition top 1%
- Electrical and Electronic Engineering top 10%
- Computer Networks and Communications top 5%
- Biomedical Engineering top 10%
- Artificial Intelligence top 5%
- Co-authors
- Michael L. LittmanNikhil DandekarLiviu IftodeJames ScottMarco GruteserFabio PicconiSrimat ChakradharMário Gerla
- Topics
- Advanced Data Storage Technologies (4 papers)Parallel Computing and Optimization Techniques (4 papers)Context-Aware Activity Recognition Systems (4 papers)
- Journals
- Proceedings of the VLDB EndowmentarXiv (Cornell University)Innovative Applications of Artificial Intelligence
- Partner nations
- United StatesUnited KingdomNetherlands
In The Last Decade
Nishkam Ravi
16 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 90
- Computer Vision and Pattern Recognition 821
- Electrical and Electronic Engineering 383
- Computer Networks and Communications 367
- Biomedical Engineering 331
- Artificial Intelligence 240
Countries citing papers authored by Nishkam Ravi
This map shows the geographic impact of Nishkam Ravi'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 Nishkam Ravi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nishkam Ravi more than expected).
Fields of papers citing papers by Nishkam Ravi
This network shows the impact of papers produced by Nishkam Ravi. 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 Nishkam Ravi. The network helps show where Nishkam Ravi may publish in the future.
Co-authorship network of co-authors of Nishkam Ravi
This figure shows the co-authorship network connecting the top 25 collaborators of Nishkam Ravi. A scholar is included among the top collaborators of Nishkam Ravi 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 Nishkam Ravi. Nishkam Ravi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 7 | |
| 3 | 7 | |
| 4 | 19 | |
| 5 | 10 | |
| 6 | 1 | |
| 7 | 8 | |
| 8 | 101 | |
| 9 | 27 | |
| 10 | 12 | |
| 11 | Outdoor distributed computing with split smart messages | 0 |
| 12 | 12 | |
| 13 | 12 | |
| 14 | 3 | |
| 15 | 72 | |
| 16 | Activity recognition from accelerometer databreakdown → | 1000 |
| 17 | An instance-based state representation for network repair | 12 |
About Nishkam Ravi
Nishkam Ravi is a scholar working on Hardware and Architecture, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 17 papers that have together received 1.3k indexed citations. Recurring topics across this work include Advanced Data Storage Technologies (4 papers), Parallel Computing and Optimization Techniques (4 papers) and Context-Aware Activity Recognition Systems (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (821 citations), Transportation (157 citations) and Human-Computer Interaction (106 citations). Nishkam Ravi has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Michael L. Littman, Nikhil Dandekar, Liviu Iftode, James Scott, Marco Gruteser, Fabio Picconi, Srimat Chakradhar, Mário Gerla, Stephen Smaldone and Pravin Shankar. Their work appears in journals such as Proceedings of the VLDB Endowment, arXiv (Cornell University) and Innovative Applications of Artificial Intelligence.
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.