John Peebles
Impact in
-
- Complexity and Algorithms in Graphs
- Advanced Graph Theory Research
Papers in
-
- Complexity and Algorithms in Graphs 6
- Advanced Graph Theory Research 2
-
- Algorithms and Data Compression 3
- Machine Learning and Algorithms 3
- Co-authors
- Anup Rao (4 shared papers)Aaron Sidford (3 shared papers)Jonathan A. Kelner (3 shared papers)Michael B. Cohen (3 shared papers)Richard Peng (4 shared papers)Adrian Vladu (2 shared papers)Rasmus Kyng (2 shared papers)Sushant Sachdeva (1 shared paper)
- Journals
- Algorithmica (1 paper)SIAM Journal on Computing (1 paper)Lecture notes in computer science (1 paper)DSpace@MIT (Massachusetts Institute of Technology) (2 papers)arXiv (Cornell University) (2 papers)
- Partner nations
- United StatesIsrael
In The Last Decade
John Peebles
12 papers receiving 118 citations
Peers
Comparison fields: 5 of 39
- Computational Mathematics 4
- Computational Theory and Mathematics 69
- Statistics and Probability 30
- Statistical and Nonlinear Physics 28
- Artificial Intelligence 57
Countries citing papers authored by John Peebles
This map shows the geographic impact of John Peebles'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 John Peebles with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Peebles more than expected).
Fields of papers citing papers by John Peebles
This network shows the impact of papers produced by John Peebles. 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 John Peebles. The network helps show where John Peebles may publish in the future.
Co-authors
The 19 scholars most cited alongside John Peebles, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 30 | |
| 2 | Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More | 2016 | 24 |
| 3 | 2017 | 21 | |
| 4 | 2017 | 15 | |
| 5 | 2018 | 10 | |
| 6 | Towards Understanding the Dynamics of Generative Adversarial Networks. | 2017 | 9 |
| 7 | HMC CS Technical Report CS-2011-1: Faster Dynamic Programming Algorithms for the Cophylogeny Reconstruction Problem | 2011 | 5 |
| 8 | 2019 | 4 | |
| 9 | Testing Identity of Multidimensional Histograms | 2018 | 3 |
| 10 | 2016 | 3 | |
| 11 | 2020 | 3 | |
| 12 | 2015 | 2 |
About John Peebles
John Peebles is a scholar working on Computational Theory and Mathematics, Artificial Intelligence, Statistics and Probability, Geometry and Topology and Infectious Diseases, having authored 12 papers that have together received 129 indexed citations. Recurring topics across this work include Complexity and Algorithms in Graphs (6 papers), Markov Chains and Monte Carlo Methods (5 papers), Graph theory and applications (3 papers), Algorithms and Data Compression (3 papers), Machine Learning and Algorithms (3 papers), Advanced Graph Theory Research (2 papers), SARS-CoV-2 detection and testing (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). The work is most often cited by research in Computational Mathematics (4 citations), Computational Theory and Mathematics (69 citations), Statistics and Probability (30 citations), Statistical and Nonlinear Physics (28 citations) and Artificial Intelligence (57 citations). John Peebles has collaborated with scholars based in United States and Israel. Frequent co-authors include Anup Rao, Aaron Sidford, Jonathan A. Kelner, Michael B. Cohen, Richard Peng, Adrian Vladu, Rasmus Kyng, Sushant Sachdeva, Anak Yodpinyanee and Ronitt Rubinfeld. Their work appears in journals such as Algorithmica, SIAM Journal on Computing, Lecture notes in computer science, DSpace@MIT (Massachusetts Institute of Technology) and arXiv (Cornell University).
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.