Ravi Montenegro
- Statistics and Probability top 2%
- Mathematical Physics top 10%
- Artificial Intelligence
- Statistical and Nonlinear Physics top 10%
- Computational Theory and Mathematics top 10%
- Topics
- Markov Chains and Monte Carlo Methods (12 papers)Stochastic processes and statistical mechanics (7 papers)Point processes and geometric inequalities (5 papers)
- Journals
- The Annals of Applied ProbabilityIsrael Journal of MathematicsRandom Structures and Algorithms
- Partner nations
- United StatesSouth KoreaUnited Kingdom
In The Last Decade
Ravi Montenegro
17 papers receiving 292 citations
Peers
Comparison fields: 5 of 53
- Statistics and Probability 157
- Mathematical Physics 107
- Artificial Intelligence 79
- Statistical and Nonlinear Physics 55
- Computational Theory and Mathematics 54
Countries citing papers authored by Ravi Montenegro
This map shows the geographic impact of Ravi Montenegro'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 Ravi Montenegro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ravi Montenegro more than expected).
Fields of papers citing papers by Ravi Montenegro
This network shows the impact of papers produced by Ravi Montenegro. 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 Ravi Montenegro. The network helps show where Ravi Montenegro may publish in the future.
Co-authorship network of co-authors of Ravi Montenegro
This figure shows the co-authorship network connecting the top 25 collaborators of Ravi Montenegro. A scholar is included among the top collaborators of Ravi Montenegro 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 Ravi Montenegro. Ravi Montenegro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 12 | |
| 2 | 5 | |
| 3 | 4 | |
| 4 | 3 | |
| 5 | 2 | |
| 6 | Generalized Cheeger inequalities for eigenvalues of non-reversible Markov chains | 1 |
| 7 | A Near Optimal Bound for Pollard's Rho to Solve Discrete Log | 1 |
| 8 | Eigenvalues of non-reversible Markov chains: their connection to mixing times, reversible Markov chains, and Cheeger inequalities | 2 |
| 9 | Mathematical Aspects of Mixing Times in Markov Chains (Foundations and Trends(R) in Theoretical Computer Science) | 24 |
| 10 | 11 | |
| 11 | 109 | |
| 12 | 1 | |
| 13 | 33 | |
| 14 | 102 | |
| 15 | 2 | |
| 16 | Faster Mixing by Isoperimetric Inequalities | 3 |
| 17 | 7 |
About Ravi Montenegro
Ravi Montenegro is a scholar working on Statistics and Probability, Mathematical Physics and Applied Mathematics, having authored 17 papers that have together received 322 indexed citations. Recurring topics across this work include Markov Chains and Monte Carlo Methods (12 papers), Stochastic processes and statistical mechanics (7 papers) and Point processes and geometric inequalities (5 papers). The work is most often cited by research in Statistics and Probability (157 citations), Mathematical Physics (107 citations) and Discrete Mathematics and Combinatorics (18 citations). Ravi Montenegro has collaborated with scholars based in United States, South Korea and United Kingdom. Frequent co-authors include Prasad Tetali, Sharad Goel, Ravi Kannan, László Lovász, Jeong Han Kim and Yuval Peres. Their work appears in journals such as The Annals of Applied Probability, Israel Journal of Mathematics and Random Structures and Algorithms.
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.