David J. Barsky
Impact in
- Mathematical Physics top 2%
- Stochastic processes and statistical mechanics
- Mathematical Dynamics and Fractals
- Statistics and Probability top 1%
- Markov Chains and Monte Carlo Methods
- Random Matrices and Applications
Papers in
-
- Stochastic processes and statistical mechanics 6
-
- Theoretical and Computational Physics 5
- Co-authors
- Michael Aizenman (3 shared papers)Charles M. Newman (1 shared paper)Geoffrey Grimmett (1 shared paper)Alberto Gandolfi (1 shared paper)
- Journals
- Journal of Statistical Physics (2 papers)The Annals of Probability (1 paper)Communications in Mathematical Physics (1 paper)Probability Theory and Related Fields (1 paper)The Annals of Applied Probability (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
David J. Barsky
7 papers receiving 367 citations
Peers
Comparison fields: 5 of 39
- Mathematical Physics 364
- Statistics and Probability 273
- Condensed Matter Physics 279
- Statistical and Nonlinear Physics 74
- Discrete Mathematics and Combinatorics 13
Countries citing papers authored by David J. Barsky
This map shows the geographic impact of David J. Barsky'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 David J. Barsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David J. Barsky more than expected).
Fields of papers citing papers by David J. Barsky
This network shows the impact of papers produced by David J. Barsky. 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 David J. Barsky. The network helps show where David J. Barsky may publish in the future.
Co-authors
The 4 scholars most cited alongside David J. Barsky, 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 | 1987 | 194 | |
| 2 | 1987 | 93 | |
| 3 | 1991 | 71 | |
| 4 | 1991 | 33 | |
| 5 | 1998 | 11 | |
| 6 | Critical Points and Critical Exponents in Percolation and Ising-Type Models. | 1987 | 1 |
| 7 | 1995 | 1 |
About David J. Barsky
David J. Barsky is a scholar working on Mathematical Physics, Condensed Matter Physics, Statistics and Probability, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 7 papers that have together received 404 indexed citations. Recurring topics across this work include Stochastic processes and statistical mechanics (6 papers), Theoretical and Computational Physics (5 papers), Markov Chains and Monte Carlo Methods (5 papers), Complex Network Analysis Techniques (2 papers), Statistical Methods and Inference (1 paper), Bayesian Methods and Mixture Models (1 paper) and Opinion Dynamics and Social Influence (1 paper). The work is most often cited by research in Mathematical Physics (364 citations), Statistics and Probability (273 citations), Condensed Matter Physics (279 citations), Statistical and Nonlinear Physics (74 citations) and Discrete Mathematics and Combinatorics (13 citations). David J. Barsky has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Michael Aizenman, Charles M. Newman, Geoffrey Grimmett and Alberto Gandolfi. Their work appears in journals such as Journal of Statistical Physics, The Annals of Probability, Communications in Mathematical Physics, Probability Theory and Related Fields and The Annals of Applied Probability.
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.