Keshav Santhanam
About
In The Last Decade
Keshav Santhanam
13 papers receiving 827 citations
Hit Papers
Peers
Comparison fields: 5 of 70
- Atmospheric Science 453
- Global and Planetary Change 368
- Astronomy and Astrophysics 277
- Artificial Intelligence 143
- Computer Vision and Pattern Recognition 78
Countries citing papers authored by Keshav Santhanam
This map shows the geographic impact of Keshav Santhanam'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 Keshav Santhanam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keshav Santhanam more than expected).
Fields of papers citing papers by Keshav Santhanam
This network shows the impact of papers produced by Keshav Santhanam. 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 Keshav Santhanam. The network helps show where Keshav Santhanam may publish in the future.
Co-authorship network of co-authors of Keshav Santhanam
This figure shows the co-authorship network connecting the top 25 collaborators of Keshav Santhanam. A scholar is included among the top collaborators of Keshav Santhanam 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 Keshav Santhanam. Keshav Santhanam 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 | 10 | |
| 4 | 2 | |
| 5 | 114 | |
| 6 | 20 | |
| 7 | 6 | |
| 8 | Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads | 25 |
| 9 | Analysis and Exploitation of Dynamic Pricing in the Public Cloud for ML Training | 5 |
| 10 | Accelerating Deep Learning Workloads Through Efficient Multi-Model Execution | 12 |
| 11 | Rapid calculation of radiative heating rates and photodissociation rates in inhomogeneous multiple scattering atmospheres breakdown → | 677 |
| 12 | 2 | |
| 13 | 26 | |
| 14 | 8 |
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.