Erick Chastain

484 citations
11 papers · 296 · h-index 6

Impact in

Papers in

Erick Chastain

11 papers receiving 283 citations

Peers

Erick Chastain
Comparison fields: 5 of 64
  • Statistical and Nonlinear Physics 120
  • Computer Networks and Communications 112
  • Computational Theory and Mathematics 28
  • Management Science and Operations Research 21
  • Molecular Biology 86
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Jiandong Zhu China
D.A. Miller United States
Qunxi Zhu China
Michael Hörnquist Sweden
Lee DeVille United States
Liangjie Sun China
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Citations per field
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Citations per year

Countries citing papers authored by Erick Chastain

Since Specialization
Citations

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

Fields of papers citing papers by Erick Chastain

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 22 scholars most cited alongside Erick Chastain, 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 Erick Chastain Line = papers co-authored together Erick Chastain links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1 2012208
2 201447
3
Active Learning as Non-Convex Optimization
20098
4 20237
5 20067
6 20136
7
Planning in Reward-Rich Domains via PAC Bandits
20124
8 20234
9
Controllability of Real Networks
20112
10
Nodal dynamics determine the controllability of complex networks
20112
11 20171

About Erick Chastain

Erick Chastain is a scholar working on Artificial Intelligence, Molecular Biology, Management Science and Operations Research, Computer Networks and Communications and Statistical and Nonlinear Physics, having authored 11 papers that have together received 296 indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (4 papers), Artificial Intelligence in Games (2 papers), Game Theory and Applications (2 papers), Evolution and Genetic Dynamics (2 papers), Opinion Dynamics and Social Influence (2 papers), Energy Efficient Wireless Sensor Networks (1 paper), Neural Networks Stability and Synchronization (1 paper) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (120 citations), Computer Networks and Communications (112 citations), Computational Theory and Mathematics (28 citations), Management Science and Operations Research (21 citations) and Molecular Biology (86 citations). Erick Chastain has collaborated with scholars based in United States, Netherlands and Japan. Frequent co-authors include Carl T. Bergstrom, J.S. Freudenberg, Daril A. Vilhena, Noah J. Cowan, Adi Livnat, Umesh Vazirani, Christos Papadimitriou, Yanxi Liu, Jeff Bilmes and Andrew Guillory. Their work appears in journals such as Neurocomputing, Proceedings of the National Academy of Sciences, Biosystems, PLoS ONE and Entropy.

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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