Raghu Meka

2.0k citations
48 papers · 792 · h-index 17

Impact in

Papers in

    • Machine Learning and Algorithms 14
    • Cryptography and Data Security 12
    • Algorithms and Data Compression 6
    • Stochastic Gradient Optimization Techniques 5
    • Coding theory and cryptography 4
    • Complexity and Algorithms in Graphs 25
    • Advanced Graph Theory Research 5

Raghu Meka

46 papers receiving 721 citations

Peers

Raghu Meka
Comparison fields: 5 of 58
  • Computational Mathematics 31
  • Computational Theory and Mathematics 305
  • Computational Mechanics 270
  • Numerical Analysis 66
  • Computer Graphics and Computer-Aided Design 36
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Citations per field
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Citations per year

Countries citing papers authored by Raghu Meka

Since Specialization
Citations

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

Fields of papers citing papers by Raghu Meka

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Raghu Meka, 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 Raghu Meka Line = papers co-authored together Raghu Meka links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Guaranteed Rank Minimization via Singular Value Projection
2010240
2 200845
3 201035
4 201233
5 201231
6 200831
7 201229
8 201529
9
Matrix Completion from Power-Law Distributed Samples
200927
10 201025
11 201225
12 201121
13 201520
14 201318
15
201616
16 200816
17 201716
18 201316
19 201215
20 201515

About Raghu Meka

Raghu Meka is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability, Discrete Mathematics and Combinatorics and Numerical Analysis, having authored 48 papers that have together received 792 indexed citations. Recurring topics across this work include Complexity and Algorithms in Graphs (25 papers), Machine Learning and Algorithms (14 papers), Cryptography and Data Security (12 papers), Markov Chains and Monte Carlo Methods (7 papers), Algorithms and Data Compression (6 papers), Stochastic Gradient Optimization Techniques (5 papers), Advanced Graph Theory Research (5 papers) and Coding theory and cryptography (4 papers). The work is most often cited by research in Computational Mathematics (31 citations), Computational Theory and Mathematics (305 citations), Computational Mechanics (270 citations), Numerical Analysis (66 citations) and Computer Graphics and Computer-Aided Design (36 citations). Raghu Meka has collaborated with scholars based in United States, India and Israel. Frequent co-authors include Inderjit S. Dhillon, Prateek Jain, David Zuckerman, Shachar Lovett, Parikshit Gopalan, Adam R. Klivans, Prateek Jain, Prasad Raghavendra, Omer Reingold and Constantine Caramanis. Their work appears in journals such as SIAM Journal on Computing, Journal of the ACM, Statistical Analysis and Data Mining The ASA Data Science Journal, Theory of Computing and Computational Complexity.

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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