John Mount
- Computational Theory and Mathematics top 5%
- Artificial Intelligence
- Statistics and Probability top 5%
- Molecular Biology
- Spectroscopy
- Co-authors
- Nina ZumelRavi KannanMartin DyerJim RuppertAjay N. JainRobert V. StantonJennifer L. MillerSridhar Tayur
- Topics
- Markov Chains and Monte Carlo Methods (5 papers)Chemical Synthesis and Analysis (3 papers)Computational Drug Discovery Methods (3 papers)
- Cited by
- Discrete Mathematics and CombinatoricsStatistics and ProbabilityComputational Theory and Mathematics
- Journals
- Journal of Medicinal ChemistryMathematics of Operations ResearchJournal of Molecular Graphics and Modelling
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
John Mount
14 papers receiving 236 citations
Peers
Comparison fields: 5 of 116
- Computational Theory and Mathematics 85
- Artificial Intelligence 62
- Statistics and Probability 53
- Molecular Biology 37
- Spectroscopy 26
Countries citing papers authored by John Mount
This map shows the geographic impact of John Mount'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 Mount with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Mount more than expected).
Fields of papers citing papers by John Mount
This network shows the impact of papers produced by John Mount. 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 Mount. The network helps show where John Mount may publish in the future.
Co-authorship network of co-authors of John Mount
This figure shows the co-authorship network connecting the top 25 collaborators of John Mount. A scholar is included among the top collaborators of John Mount 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 John Mount. John Mount is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Succinct and Correct Statistical Summaries for Reports [R package sigr version 1.1.3] | 1 |
| 2 | 2 | |
| 3 | 4 | |
| 4 | Practical Data Science with R | 82 |
| 5 | The equivalence of logistic regression and maximum entropymodels | 24 |
| 6 | 14 | |
| 7 | 4 | |
| 8 | 10 | |
| 9 | 1 | |
| 10 | 38 | |
| 11 | 4 | |
| 12 | 63 | |
| 13 | 9 | |
| 14 | Integrating Efficient Model-Learning and Problem-Solving Algorithms in Permutation Environments. | 4 |
About John Mount
John Mount is a scholar working on Statistics and Probability, Computational Theory and Mathematics and Discrete Mathematics and Combinatorics, having authored 14 papers that have together received 260 indexed citations. Recurring topics across this work include Markov Chains and Monte Carlo Methods (5 papers), Chemical Synthesis and Analysis (3 papers) and Computational Drug Discovery Methods (3 papers). The work is most often cited by research in Discrete Mathematics and Combinatorics (21 citations), Statistics and Probability (53 citations) and Computational Theory and Mathematics (85 citations). John Mount has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Nina Zumel, Ravi Kannan, Martin Dyer, Jim Ruppert, Ajay N. Jain, Robert V. Stanton, Jennifer L. Miller, Sridhar Tayur, Prasad Chalasani and Oren Etzioni. Their work appears in journals such as Journal of Medicinal Chemistry, Mathematics of Operations Research and Journal of Molecular Graphics and Modelling.
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.