Ari Pakman

1.2k citations
26 papers · 578 · h-index 12

Impact in

Papers in

Ari Pakman

25 papers receiving 564 citations

Peers

Ari Pakman
Comparison fields: 5 of 77
  • Nuclear and High Energy Physics 323
  • Astronomy and Astrophysics 194
  • Statistical and Nonlinear Physics 144
  • Developmental Neuroscience 25
  • Geometry and Topology 59
Replace Takuya Kanazawa with:
Takuya Kanazawa Japan
P. Białas Poland
Dmitry Krotov United States
Robin Forman United States
James F. Glazebrook United States
Christoph Rahmede United Kingdom
Nele Vandersickel Belgium
Victor G. LeBlanc Canada
K. Mallick France
Stephan Stolz United States
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Citations per field
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Citations per year

Countries citing papers authored by Ari Pakman

Since Specialization
Citations

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

Fields of papers citing papers by Ari Pakman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ari Pakman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ari Pakman Line = papers co-authored together Ari Pakman links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201398
2 201659
3 201058
4 200955
5 200951
6 201337
7 200534
8 200733
9 200328
10 200528
11
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits
201319
12 199912
13 200610
14 201310
15 20009
16
Estimating the Unique Information of Continuous Variables.
20218
17 20077
18
Stochastic Bouncy Particle Sampler
20176
19 20005
20 20063

About Ari Pakman

Ari Pakman is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence, Geometry and Topology, Statistical and Nonlinear Physics and Cognitive Neuroscience, having authored 26 papers that have together received 578 indexed citations. Recurring topics across this work include Black Holes and Theoretical Physics (12 papers), Algebraic structures and combinatorial models (6 papers), Neural dynamics and brain function (5 papers), Neural Networks and Applications (5 papers), Markov Chains and Monte Carlo Methods (4 papers), Bayesian Methods and Mixture Models (4 papers), Quantum Chromodynamics and Particle Interactions (3 papers) and Cosmology and Gravitation Theories (3 papers). The work is most often cited by research in Nuclear and High Energy Physics (323 citations), Astronomy and Astrophysics (194 citations), Statistical and Nonlinear Physics (144 citations), Developmental Neuroscience (25 citations) and Geometry and Topology (59 citations). Ari Pakman has collaborated with scholars based in United States, Israel and France. Frequent co-authors include Liam Paninski, Leonardo Rastelli, Shlomo S. Razamat, Atish Dabholkar, Amit Sever, Dan Israël, Jan Troost, Thomas M. Jessell, Jay B. Bikoff and L. F. Abbott. Their work appears in journals such as Journal of High Energy Physics, Physics Letters B, Nuclear Physics B, Advances in Theoretical and Mathematical Physics and Journal of Computational Neuroscience.

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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