Keith Erickson

1.1k total citations
39 papers, 566 citations indexed

About

Keith Erickson is a scholar working on Nuclear and High Energy Physics, Computer Networks and Communications and Aerospace Engineering. According to data from OpenAlex, Keith Erickson has authored 39 papers receiving a total of 566 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Nuclear and High Energy Physics, 14 papers in Computer Networks and Communications and 14 papers in Aerospace Engineering. Recurrent topics in Keith Erickson's work include Magnetic confinement fusion research (29 papers), Superconducting Materials and Applications (10 papers) and Advanced Data Storage Technologies (8 papers). Keith Erickson is often cited by papers focused on Magnetic confinement fusion research (29 papers), Superconducting Materials and Applications (10 papers) and Advanced Data Storage Technologies (8 papers). Keith Erickson collaborates with scholars based in United States, South Korea and China. Keith Erickson's co-authors include Mark D. Boyer, Cristina Rea, Egemen Kolemen, R. Granetz, Kevin Montes, R. A. Tinguely, S. Kaye, N.W. Eidietis, Bingjia Xiao and R. L. Frost and has published in prestigious journals such as Nature, Review of Scientific Instruments and IEEE Transactions on Aerospace and Electronic Systems.

In The Last Decade

Keith Erickson

33 papers receiving 539 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Keith Erickson United States 13 325 154 119 114 79 39 566
Dalong Chen China 13 327 1.0× 127 0.8× 107 0.9× 99 0.9× 80 1.0× 62 493
Cristina Rea United States 15 473 1.5× 186 1.2× 137 1.2× 147 1.3× 82 1.0× 39 657
A. Pau Switzerland 14 332 1.0× 133 0.9× 101 0.8× 109 1.0× 57 0.7× 40 454
Kevin Montes United States 9 272 0.8× 119 0.8× 91 0.8× 92 0.8× 46 0.6× 11 378
M. Johnson United Kingdom 10 571 1.8× 166 1.1× 207 1.7× 103 0.9× 134 1.7× 18 706
G. Vagliasindi Italy 11 191 0.6× 92 0.6× 51 0.4× 94 0.8× 41 0.5× 33 404
O. Barana Italy 11 292 0.9× 103 0.7× 97 0.8× 24 0.2× 98 1.2× 38 370
J. Stillerman United States 14 351 1.1× 110 0.7× 53 0.4× 30 0.3× 115 1.5× 71 587
A. Pereira Spain 12 179 0.6× 83 0.5× 54 0.5× 118 1.0× 19 0.2× 50 462
M. Zilker Germany 12 331 1.0× 138 0.9× 76 0.6× 16 0.1× 99 1.3× 45 458

Countries citing papers authored by Keith Erickson

Since Specialization
Citations

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

Fields of papers citing papers by Keith Erickson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Keith Erickson

This figure shows the co-authorship network connecting the top 25 collaborators of Keith Erickson. A scholar is included among the top collaborators of Keith Erickson 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 Keith Erickson. Keith Erickson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Erickson, Keith, et al.. (2025). Experimental demonstration of real-time electron temperature profile control in DIII-D. Nuclear Fusion. 65(10). 106031–106031.
2.
Woo, M.H., Chun Sung Byun, S.K. Kim, et al.. (2025). TorbeamNN: machine learning-based steering of ECH mirrors on KSTAR. Plasma Physics and Controlled Fusion. 67(5). 55036–55036.
3.
Yang, S.M., S. Munaretto, Qiming Hu, et al.. (2025). Stability evaluation and mitigation strategies in advanced tokamaks using 3D MHD spectroscopy. Nuclear Fusion. 65(9). 96015–96015.
4.
5.
Sabbagh, S.A., J.W. Berkery, Y.S. Park, et al.. (2024). DECAF cross-device characterization of tokamak disruptions indicated by abnormalities in plasma vertical position and current. Nuclear Fusion. 64(6). 66030–66030. 2 indexed citations
6.
Seo, Jaemin, et al.. (2024). Avoiding fusion plasma tearing instability with deep reinforcement learning. Nature. 626(8000). 746–751. 50 indexed citations
7.
Eldon, D., L. Casali, I. Bykov, et al.. (2024). Characterization and controllability of radiated power via extrinsic impurity seeding in strongly negative triangularity plasmas in DIII-D. Plasma Physics and Controlled Fusion. 67(1). 15018–15018. 4 indexed citations
8.
Jalalvand, Azarakhsh, et al.. (2024). Initial testing of Alfvén eigenmode feedback control with machine-learning observers on DIII-D. Nuclear Fusion. 64(9). 96020–96020. 4 indexed citations
9.
Penaflor, B.G., B. Sammuli, D.A. Piglowski, et al.. (2024). Recent Advancements in the DIII-D Plasma Control System. IEEE Transactions on Plasma Science. 52(9). 3535–3541.
10.
Tang, W. M., Ge Dong, J.L. Barr, et al.. (2023). Implementation of AI/DEEP learning disruption predictor into a plasma control system. Contributions to Plasma Physics. 63(5-6). 2 indexed citations
11.
Erickson, Keith, et al.. (2023). A general infrastructure for data-driven control design and implementation in tokamaks. Journal of Plasma Physics. 89(1). 4 indexed citations
12.
Seo, Jaemin, et al.. (2023). Machine learning-based real-time kinetic profile reconstruction in DIII-D. Nuclear Fusion. 64(2). 26006–26006. 15 indexed citations
13.
Erickson, Keith, et al.. (2021). Keras2c: A library for converting Keras neural networks to real-time compatible C. Engineering Applications of Artificial Intelligence. 100. 104182–104182. 34 indexed citations
14.
Rea, Cristina, Kevin Montes, Wenhui Hu, et al.. (2020). Interpretable data-driven disruption predictors to trigger avoidance and mitigation actuators on different tokamaks. APS Division of Plasma Physics Meeting Abstracts. 2020. 1 indexed citations
15.
Erickson, Keith, B. A. Grierson, D. C. Pace, et al.. (2019). Feedback control of stored energy and rotation with variable beam energy and perveance on DIII-D. Nuclear Fusion. 59(7). 76004–76004. 8 indexed citations
16.
Montes, Kevin, Cristina Rea, R. Granetz, et al.. (2019). Machine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST. Nuclear Fusion. 59(9). 96015–96015. 77 indexed citations
17.
Boyer, Mark D., D. J. Battaglia, D. Mueller, et al.. (2018). Plasma boundary shape control and real-time equilibrium reconstruction on NSTX-U. Nuclear Fusion. 58(3). 36016–36016. 23 indexed citations
18.
Erickson, Keith, S. P. Gerhardt, J. Lawson, et al.. (2015). NSTX-U Digital Coil Protection System integration with existing Plasma Control System. 1–4. 1 indexed citations
19.
Erickson, Keith, et al.. (2014). NSTX-U Control System Upgrades. Fusion Engineering and Design. 89(6). 853–858. 8 indexed citations
20.
Erickson, Keith, et al.. (2004). Combinatoric collaboration on Costas arrays and radar applications. 260–265. 22 indexed citations

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.

Explore authors with similar magnitude of impact

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