Auralee Edelen

996 total citations
38 papers, 423 citations indexed

About

Auralee Edelen is a scholar working on Electrical and Electronic Engineering, Aerospace Engineering and Nuclear and High Energy Physics. According to data from OpenAlex, Auralee Edelen has authored 38 papers receiving a total of 423 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Electrical and Electronic Engineering, 21 papers in Aerospace Engineering and 16 papers in Nuclear and High Energy Physics. Recurrent topics in Auralee Edelen's work include Particle Accelerators and Free-Electron Lasers (27 papers), Particle accelerators and beam dynamics (21 papers) and Advanced X-ray Imaging Techniques (8 papers). Auralee Edelen is often cited by papers focused on Particle Accelerators and Free-Electron Lasers (27 papers), Particle accelerators and beam dynamics (21 papers) and Advanced X-ray Imaging Techniques (8 papers). Auralee Edelen collaborates with scholars based in United States, United Kingdom and Slovenia. Auralee Edelen's co-authors include Claudio Emma, Adi Hanuka, Daniel Ratner, Alberto Lutman, Alexander Scheinker, S.G. Biedroń, S.V. Milton, Dorian Bohler, B. O’Shea and G. White and has published in prestigious journals such as Physical Review Letters, Scientific Reports and Optics Express.

In The Last Decade

Auralee Edelen

34 papers receiving 416 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Auralee Edelen United States 9 201 166 131 119 57 38 423
Zhiyong Qin China 10 180 0.9× 411 2.5× 50 0.4× 118 1.0× 5 0.1× 40 582
Changchun Sun United States 12 148 0.7× 221 1.3× 130 1.0× 154 1.3× 3 0.1× 64 507
Irène Waldspurger France 4 90 0.4× 8 0.0× 35 0.3× 236 2.0× 41 0.7× 5 390
B. Sammuli United States 10 40 0.2× 241 1.5× 75 0.6× 42 0.4× 2 0.0× 29 326
J. Irwin United States 13 303 1.5× 86 0.5× 202 1.5× 35 0.3× 2 0.0× 103 682
A. Levi United States 6 45 0.2× 10 0.1× 37 0.3× 120 1.0× 20 0.4× 9 296
Sarah Cousineau United States 9 199 1.0× 162 1.0× 242 1.8× 76 0.6× 1 0.0× 83 378
Stephen R. Gottesman United States 5 62 0.3× 59 0.4× 59 0.5× 206 1.7× 3 0.1× 13 432
Stefano Redaelli Switzerland 14 494 2.5× 452 2.7× 277 2.1× 66 0.6× 2 0.0× 243 892
Brady Benware United States 21 992 4.9× 276 1.7× 8 0.1× 123 1.0× 24 0.4× 58 1.4k

Countries citing papers authored by Auralee Edelen

Since Specialization
Citations

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

Fields of papers citing papers by Auralee Edelen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Auralee Edelen

This figure shows the co-authorship network connecting the top 25 collaborators of Auralee Edelen. A scholar is included among the top collaborators of Auralee Edelen 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 Auralee Edelen. Auralee Edelen 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.
Ma, Desheng, Steven E. Zeltmann, Zhaslan Baraissov, et al.. (2025). Emittance minimization for aberration correction II: Physics-informed Bayesian optimization of an electron microscope. Ultramicroscopy. 273. 114138–114138. 2 indexed citations
2.
Martínez, José Luis, et al.. (2025). Leveraging prior mean models for faster Bayesian optimization of particle accelerators. Scientific Reports. 15(1). 12232–12232.
3.
Lin, Sen, et al.. (2025). Outlook towards deployable continual learning for particle accelerators. Machine Learning Science and Technology. 6(3). 31001–31001.
4.
Schram, Malachi, et al.. (2025). Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators. Machine Learning Science and Technology. 6(2). 25018–25018. 1 indexed citations
5.
Edelen, Auralee, et al.. (2024). Machine learning for reducing noise in RF control signals at industrial accelerators. Journal of Instrumentation. 19(11). P11015–P11015.
6.
Wisniewski, Eric, et al.. (2024). Efficient six-dimensional phase space reconstructions from experimental measurements using generative machine learning. Physical Review Accelerators and Beams. 27(9). 7 indexed citations
7.
Edelen, Auralee & Xiaobiao Huang. (2024). Machine Learning for Design and Control of Particle Accelerators: A Look Backward and Forward. Annual Review of Nuclear and Particle Science. 74(1). 557–581. 4 indexed citations
8.
Edelen, Auralee, et al.. (2023). Demonstration of Autonomous Emittance Characterization at the Argonne Wakefield Accelerator. Instruments. 7(3). 29–29. 1 indexed citations
9.
Neiswanger, Willie, Claudio Emma, Christopher Mayes, et al.. (2023). Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives. Machine Learning Science and Technology. 5(1). 15004–15004. 2 indexed citations
10.
Edelen, Auralee, et al.. (2023). Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations. Physical Review Letters. 130(14). 145001–145001. 21 indexed citations
11.
Ma, Desheng, Chenyu Zhang, Yu‐Tsun Shao, et al.. (2023). Physics-informed Bayesian Optimization of an Electron Microscope. Microscopy and Microanalysis. 29(Supplement_1). 1875–1877. 1 indexed citations
12.
Edelen, Auralee, et al.. (2022). Differentiable Preisach Modeling for Characterization and Optimization of Particle Accelerator Systems with Hysteresis. Physical Review Letters. 128(20). 204801–204801. 13 indexed citations
13.
14.
Edelen, Auralee, et al.. (2022). PyEmittance: A General Python Package for Particle Beam Emittance Measurements With Adaptive Quadrupole Scans [Poster]. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations
15.
Baraissov, Zhaslan, et al.. (2021). Aberration Corrector Tuning with Machine-Learning-Based Emittance Measurements and Bayesian Optimization. Microscopy and Microanalysis. 27(S1). 810–812. 10 indexed citations
16.
Hanuka, Adi, et al.. (2021). Multiobjective Bayesian optimization for online accelerator tuning. Physical Review Accelerators and Beams. 24(6). 26 indexed citations
17.
Duris, Joseph, Adi Hanuka, Auralee Edelen, et al.. (2020). Bayesian Optimization of a Free-Electron Laser. Physical Review Letters. 124(12). 124801–124801. 83 indexed citations
18.
Scheinker, Alexander, Auralee Edelen, Dorian Bohler, Claudio Emma, & Alberto Lutman. (2018). Demonstration of Model-Independent Control of the Longitudinal Phase Space of Electron Beams in the Linac-Coherent Light Source with Femtosecond Resolution. Physical Review Letters. 121(4). 44801–44801. 52 indexed citations
19.
Emma, Claudio, Auralee Edelen, Mark Hogan, et al.. (2018). Machine learning-based longitudinal phase space prediction of particle accelerators. Physical Review Accelerators and Beams. 21(11). 59 indexed citations
20.
Edelen, Auralee, et al.. (2016). First Principles Modeling of RFQ Cooling System and Resonant Frequency Responses for Fermilab’s PIP-II Injector Test. IEEE Transactions on Nuclear Science. 64(2). 800–808. 1 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.

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