Umesh Vazirani

101 papers receiving 7.3k citations

Hit Papers

An Introduction to Computational Learning Theory199320262004201519941993199719972014250500750

Peers

Umesh Vazirani
Comparison fields: 5 of 125
  • Artificial Intelligence 5.4k
  • Computational Theory and Mathematics 3.2k
  • Atomic and Molecular Physics, and Optics 2.0k
  • Computer Networks and Communications 1.4k
  • Management Science and Operations Research 643
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Citations per field
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Citations per year

Countries citing papers authored by Umesh Vazirani

Since Specialization
Citations

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

Fields of papers citing papers by Umesh Vazirani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Umesh Vazirani

This figure shows the co-authorship network connecting the top 25 collaborators of Umesh Vazirani. A scholar is included among the top collaborators of Umesh Vazirani 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 Umesh Vazirani. Umesh Vazirani 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 10
2 4
3
Demonstration of Interactive Protocols for Classically-Verifiable Quantum Advantage
2
4
Certifiable Randomness from a Single Quantum Device.
4
5
Fully Device-Independent Quantum Key Distributionbreakdown →
300
6 1
7 214
8 24
9 37
10 20
11
An omniscient Maxwell's demon
0
12 10
13
Efficiency Considerations in Using Semi-random Sources (Extended Abstract)
3
14 143
15
Efficient and secure pseudo-random number generation
9
16
Towards a Strong Communication Complexity Theory or Generating Quasi-Random Sequences from Two Communicating Slightly-random Sources (Extended Abstract)
10
17
NC Algorithms for Comparability Graphs, Interval Gaphs, and Testing for Unique Perfect Matching
11
18
Generating Quasi-Random Sequences from Slightly-Random Sources (Extended Abstract)
4
19
RSA Bits are 732+epsilon Secure.
1
20 18

About Umesh Vazirani

Umesh Vazirani is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Computational Mathematics, having authored 103 papers that have together received 7.9k indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (42 papers), Quantum Information and Cryptography (29 papers) and Computability, Logic, AI Algorithms (23 papers). The work is most often cited by research in Computational Theory and Mathematics (3.2k citations), Artificial Intelligence (5.4k citations) and Atomic and Molecular Physics, and Optics (2.0k citations). Umesh Vazirani has collaborated with scholars based in United States, Israel and Canada. Frequent co-authors include Ethan Bernstein, Vijay V. Vazirani, Michael Kearns, Thomas Vidick, Ketan Mulmuley, Satish Rao, Gilles Brassard, Charles H. Bennett, Andris Ambainis and Sanjeev Arora. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Physical Review Letters.

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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