Pradheep Elango
Impact in
-
- Advanced Bandit Algorithms Research
- Information Systems top 5%
- Recommender Systems and Techniques
- Web Data Mining and Analysis
Papers in
-
- Recommender Systems and Techniques 6
- Cloud Computing and Resource Management 2
-
- Advanced Bandit Algorithms Research 7
- Co-authors
- Bee-Chung Chen (7 shared papers)Deepak Agarwal (4 shared papers)Xuanhui Wang (2 shared papers)Deepak Agarwal (2 shared papers)Raghu Ramakrishnan (2 shared papers)Scott Roy (1 shared paper)Andrea C. Arpaci-Dusseau (1 shared paper)Miron Livny (1 shared paper)
- Journals
- Communications of the ACM (1 paper)International Journal of Engineering Trends and Technology (1 paper)International Journal of Computer Applications (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Pradheep Elango
9 papers receiving 359 citations
Peers
Comparison fields: 5 of 38
- Management Science and Operations Research 175
- Information Systems 233
- Marketing 51
- Computer Networks and Communications 111
- Artificial Intelligence 142
Countries citing papers authored by Pradheep Elango
This map shows the geographic impact of Pradheep Elango'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 Pradheep Elango with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pradheep Elango more than expected).
Fields of papers citing papers by Pradheep Elango
This network shows the impact of papers produced by Pradheep Elango. 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 Pradheep Elango. The network helps show where Pradheep Elango may publish in the future.
Co-authors
The 8 scholars most cited alongside Pradheep Elango, 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 | 2009 | 84 | |
| 2 | Online Models for Content Optimization | 2008 | 71 |
| 3 | 2009 | 65 | |
| 4 | 2010 | 43 | |
| 5 | 2011 | 40 | |
| 6 | 2012 | 32 | |
| 7 | Deploying virtual machines as sandboxes for the grid | 2005 | 29 |
| 8 | 2013 | 21 | |
| 9 | 2016 | 7 | |
| 10 | 2013 | 0 |
About Pradheep Elango
Pradheep Elango is a scholar working on Information Systems, Management Science and Operations Research, Computer Networks and Communications, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 10 papers that have together received 392 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (7 papers), Recommender Systems and Techniques (6 papers), Optimization and Search Problems (2 papers), Cloud Computing and Resource Management (2 papers), Distributed and Parallel Computing Systems (2 papers), Machine Learning and ELM (1 paper), Data Stream Mining Techniques (1 paper) and Distributed systems and fault tolerance (1 paper). The work is most often cited by research in Management Science and Operations Research (175 citations), Information Systems (233 citations), Marketing (51 citations), Computer Networks and Communications (111 citations) and Artificial Intelligence (142 citations). Pradheep Elango has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Bee-Chung Chen, Deepak Agarwal, Xuanhui Wang, Deepak Agarwal, Raghu Ramakrishnan, Scott Roy, Andrea C. Arpaci-Dusseau and Miron Livny. Their work appears in journals such as Communications of the ACM, International Journal of Engineering Trends and Technology, International Journal of Computer Applications and Neural Information Processing Systems.
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.