Vikram Nathan
Impact in
- Signal Processing top 10%
- Data Management and Algorithms
-
- Advanced Database Systems and Queries
- Advanced Data Storage Technologies
- Caching and Content Delivery
Papers in
-
- Advanced Database Systems and Queries 4
- Software System Performance and Reliability 1
-
- Cloud Computing and Resource Management 2
- Data Mining Algorithms and Applications 2
- Co-authors
- Tim Kraska (6 shared papers)Jialin Ding (3 shared papers)Mohammad Alizadeh (1 shared paper)Hongzi Mao (2 shared papers)Alex Beutel (1 shared paper)Samuel Madden (1 shared paper)Ed H. (1 shared paper)Hari Balakrishnan (1 shared paper)
- Journals
- Proceedings of the VLDB Endowment (2 papers)Molecular Ecology Resources (1 paper)Information Processing Letters (1 paper)DSpace@MIT (Massachusetts Institute of Technology) (2 papers)
- Partner nations
- United StatesGermanyCanada
In The Last Decade
Vikram Nathan
9 papers receiving 214 citations
Peers
Comparison fields: 5 of 28
- Signal Processing 90
- Computer Networks and Communications 138
- Information Systems 83
- Artificial Intelligence 82
- Computer Vision and Pattern Recognition 50
Countries citing papers authored by Vikram Nathan
This map shows the geographic impact of Vikram Nathan'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 Vikram Nathan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vikram Nathan more than expected).
Fields of papers citing papers by Vikram Nathan
This network shows the impact of papers produced by Vikram Nathan. 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 Vikram Nathan. The network helps show where Vikram Nathan may publish in the future.
Co-authors
The 25 scholars most cited alongside Vikram Nathan, 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 | 2020 | 78 | |
| 2 | SageDB: A Learned Database System | 2019 | 70 |
| 3 | Park: An Open Platform for Learning-Augmented Computer Systems | 2019 | 39 |
| 4 | Vesper: Measuring time-to-interactivity for web pages | 2018 | 15 |
| 5 | 2024 | 8 | |
| 6 | 2022 | 8 | |
| 7 | 2024 | 6 | |
| 8 | 2025 | 1 | |
| 9 | 2016 | 1 |
About Vikram Nathan
Vikram Nathan is a scholar working on Computer Networks and Communications, Information Systems, Signal Processing, Artificial Intelligence and Molecular Biology, having authored 9 papers that have together received 226 indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (4 papers), Data Management and Algorithms (4 papers), Cloud Computing and Resource Management (2 papers), Data Mining Algorithms and Applications (2 papers), Software System Performance and Reliability (1 paper), Green IT and Sustainability (1 paper), Reinforcement Learning in Robotics (1 paper) and Metaheuristic Optimization Algorithms Research (1 paper). The work is most often cited by research in Signal Processing (90 citations), Computer Networks and Communications (138 citations), Information Systems (83 citations), Artificial Intelligence (82 citations) and Computer Vision and Pattern Recognition (50 citations). Vikram Nathan has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Tim Kraska, Jialin Ding, Mohammad Alizadeh, Hongzi Mao, Alex Beutel, Samuel Madden, Mohammad Alizadeh, Ed H., Hari Balakrishnan and James Mickens. Their work appears in journals such as Proceedings of the VLDB Endowment, Molecular Ecology Resources, Information Processing Letters and DSpace@MIT (Massachusetts Institute of Technology).
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.