Rohan Varma

19 papers receiving 853 citations

Hit Papers

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel 2023 · 83 citations
832015202620182022100200300400

Peers

Rohan Varma
Comparison fields: 5 of 100
  • Statistical and Nonlinear Physics 260
  • Computational Mathematics 12
  • Artificial Intelligence 575
  • Computer Vision and Pattern Recognition 201
  • Hardware and Architecture 58
Replace Zhewei Yao with:
Zhewei Yao United States
Martine Schlag United States
Md. Mostofa Ali Patwary United States
Dorina Thanou Switzerland
Akshay Gadde United States
Robert W. Leland United States
Michaël Mathieu United States
Alain Bretto France
Rohan Varma relative to Zhewei Yao United States Zhewei Yao's profile →
Citations per field
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Zhewei Yao · 1×
Citations per year

Countries citing papers authored by Rohan Varma

Since Specialization
Citations

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

Fields of papers citing papers by Rohan Varma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Rohan Varma, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Rohan Varma Line = papers co-authored together Rohan Varma links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20253
2 20242
3
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
Hit paper breakdown →
202383
4 20217
5 202022
6
PyTorch distributed
Hit paper breakdown →
2020248
7 20191
8 20194
9 20193
10 20191
11 20180
12 20188
13 20175
14 20172
15 20165
16 201621
17 201511
18
Discrete Signal Processing on Graphs: Sampling Theory
Hit paper breakdown →
2015419
19 201522
20 20155

About Rohan Varma

Rohan Varma is a scholar working on Computational Mathematics, Statistical and Nonlinear Physics, Signal Processing, Artificial Intelligence and Computational Mechanics, having authored 20 papers that have together received 872 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (9 papers), Complex Network Analysis Techniques (7 papers), Sparse and Compressive Sensing Techniques (6 papers), Bayesian Modeling and Causal Inference (3 papers), Advanced Neural Network Applications (3 papers), Topic Modeling (2 papers), Bioinformatics and Genomic Networks (2 papers) and Blind Source Separation Techniques (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (260 citations), Computational Mathematics (12 citations), Artificial Intelligence (575 citations), Computer Vision and Pattern Recognition (201 citations) and Hardware and Architecture (58 citations). Rohan Varma has collaborated with scholars based in United States, India and Netherlands. Frequent co-authors include Siheng Chen, Jelena Kovačević, Aliaksei Sandryhaila, Jeff Smith, Teng Li, Li Shen, Adam Paszke, Soumith Chintala, Jelena Kovačević and Aarti Singh. Their work appears in journals such as Proceedings of the VLDB Endowment, IEEE Transactions on Signal and Information Processing over Networks, PLoS Computational Biology, BMC Systems Biology and IEEE Transactions on Circuits & Systems II Express Briefs.

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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