Muthian Sivathanu

23 papers receiving 609 citations

Peers

Muthian Sivathanu
Comparison fields: 5 of 39
  • Computer Networks and Communications 518
  • Information Systems 218
  • Hardware and Architecture 159
  • Artificial Intelligence 138
  • Computer Vision and Pattern Recognition 103
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Citations per field
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Citations per year

Countries citing papers authored by Muthian Sivathanu

Since Specialization
Citations

This map shows the geographic impact of Muthian Sivathanu'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 Muthian Sivathanu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muthian Sivathanu more than expected).

Fields of papers citing papers by Muthian Sivathanu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Muthian Sivathanu. 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 Muthian Sivathanu. The network helps show where Muthian Sivathanu may publish in the future.

Co-authorship network of co-authors of Muthian Sivathanu

This figure shows the co-authorship network connecting the top 25 collaborators of Muthian Sivathanu. A scholar is included among the top collaborators of Muthian Sivathanu 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 Muthian Sivathanu. Muthian Sivathanu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 43
2
Unsupervised Clustering using Pseudo-semi-supervised Learning
12
3
Quiver: An Informed Storage Cache for Deep Learning
24
4 87
5
INSTalytics: Cluster Filesystem Co-design for Big-data Analytics.
1
6
AutoLR: A Method for Automatic Tuning of Learning Rate
1
7 18
8 24
9 17
10 75
11 2
12
Awarded Best Student Paper! -- Improving Storage System Availability with D-GRAID
30
13
Life or death at block-level
47
14
Improving storage system availability with D-GRAID
12
15 43
16
Semantically-Smart Disk Systems
124
17 2
18 19
19 2
20 1

About Muthian Sivathanu

Muthian Sivathanu is a scholar working on Hardware and Architecture, Computer Networks and Communications and Information Systems, having authored 24 papers that have together received 631 indexed citations. Recurring topics across this work include Advanced Data Storage Technologies (17 papers), Parallel Computing and Optimization Techniques (10 papers) and Caching and Content Delivery (9 papers). The work is most often cited by research in Hardware and Architecture (159 citations), Computer Networks and Communications (518 citations) and Information Systems (218 citations). Muthian Sivathanu has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau, Lakshmi N. Bairavasundaram, Vijayan Prabhakaran, Ramachandran Ramjee, Nipun Kwatra, Shubham Chaudhary, Lidong Zhou, Somesh Jha and Florentina I. Popovici. Their work appears in journals such as ACM SIGPLAN Notices, ACM SIGOPS Operating Systems Review and ACM SIGMETRICS Performance Evaluation Review.

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.

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