Krishna Gade
Impact in
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- Advanced Database Systems and Queries
- Caching and Content Delivery
- IoT and Edge/Fog Computing
- Advanced Data Storage Technologies
- Distributed systems and fault tolerance
- Information Systems top 1%
- Cloud Computing and Resource Management
Papers in
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- Explainable Artificial Intelligence (XAI) 4
- Machine Learning and Data Classification 2
- Adversarial Robustness in Machine Learning 2
- Data Stream Mining Techniques 1
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- Web Data Mining and Analysis 2
- Cloud Computing and Resource Management 1
- Co-authors
- Karthik Ramasamy (1 shared paper)Amit K. Shukla (1 shared paper)Sanjeev Kulkarni (1 shared paper)Jason Baird Jackson (1 shared paper)Maosong Fu (1 shared paper)Dmitriy Ryaboy (1 shared paper)Jignesh M. Patel (1 shared paper)Krishnaram Kenthapadi (4 shared papers)
- Partner nations
- United StatesChina
In The Last Decade
Krishna Gade
8 papers receiving 845 citations
Krishna Gade's Hit Papers
Peers
Comparison fields: 5 of 74
- Computer Networks and Communications 568
- Information Systems 480
- Signal Processing 164
- Health Informatics 18
- Artificial Intelligence 332
Countries citing papers authored by Krishna Gade
This map shows the geographic impact of Krishna Gade'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 Krishna Gade with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Krishna Gade more than expected).
Fields of papers citing papers by Krishna Gade
This network shows the impact of papers produced by Krishna Gade. 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 Krishna Gade. The network helps show where Krishna Gade may publish in the future.
Co-authors
The 18 scholars most cited alongside Krishna Gade, 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 | Storm@twitter Hit paper breakdown → | 2014 | 599 |
| 2 | 2012 | 107 | |
| 3 | 2019 | 81 | |
| 4 | 2004 | 34 | |
| 5 | 2020 | 23 | |
| 6 | 2020 | 15 | |
| 7 | 2022 | 14 | |
| 8 | 2018 | 2 |
About Krishna Gade
Krishna Gade is a scholar working on Artificial Intelligence, Information Systems, Computer Networks and Communications, Computer Vision and Pattern Recognition and Signal Processing, having authored 8 papers that have together received 875 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (4 papers), Machine Learning and Data Classification (2 papers), Adversarial Robustness in Machine Learning (2 papers), Web Data Mining and Analysis (2 papers), Data Stream Mining Techniques (1 paper), Data Management and Algorithms (1 paper), Cloud Computing and Resource Management (1 paper) and Distributed systems and fault tolerance (1 paper). The work is most often cited by research in Computer Networks and Communications (568 citations), Information Systems (480 citations), Signal Processing (164 citations), Health Informatics (18 citations) and Artificial Intelligence (332 citations). Krishna Gade has collaborated with scholars based in United States and China. Frequent co-authors include Karthik Ramasamy, Amit K. Shukla, Sanjeev Kulkarni, Jason Baird Jackson, Maosong Fu, Dmitriy Ryaboy, Jignesh M. Patel, Krishnaram Kenthapadi, Ankur Taly and Sahin Cem Geyik.
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.