Raghu Meka

2.1k citations
48 papers · 811 · h-index 17

Impact in

Papers in

Raghu Meka

47 papers receiving 735 citations

Peers

Raghu Meka
Comparison fields: 5 of 59
  • Computational Mathematics 32
  • Computational Theory and Mathematics 316
  • Computational Mechanics 272
  • Numerical Analysis 65
  • Computer Graphics and Computer-Aided Design 38
Replace Ravindran Kannan with:
Ravindran Kannan United States
Daniel M. Kane United States
Jelani Nelson United States
Yin Tat Lee United States
David Steurer United States
Luis Rademacher United States
Alain Pajor France
Huy L. Nguyên United States
Aaron Sidford United States
Nicholas J. A. Harvey United States
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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 201036
4 201233
5 200831
6 201231
7 201230
8
Matrix Completion from Power-Law Distributed Samples
200928
9 201528
10 201026
11 201224
12 201522
13 201121
14 201319
15 201317
16 200817
17 201717
18
201616
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 811 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 Mathematical Approximation and Integration (4 papers). The work is most often cited by research in Computational Mathematics (32 citations), Computational Theory and Mathematics (316 citations), Computational Mechanics (272 citations), Numerical Analysis (65 citations) and Computer Graphics and Computer-Aided Design (38 citations). Raghu Meka has collaborated with scholars based in United States, United Kingdom and India. Frequent co-authors include Inderjit S. Dhillon, Prateek Jain, David Zuckerman, Shachar Lovett, Parikshit Gopalan, Adam R. Klivans, Prasad Raghavendra, Prateek Jain, Omer Reingold and Russell Impagliazzo. Their work appears in journals such as SIAM Journal on Computing, Statistical Analysis and Data Mining The ASA Data Science Journal, Journal of the ACM, Computational Complexity and Theory of Computing.

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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