Miles Cranmer

1.5k total citations
23 papers, 363 citations indexed

About

Miles Cranmer is a scholar working on Astronomy and Astrophysics, Artificial Intelligence and Instrumentation. According to data from OpenAlex, Miles Cranmer has authored 23 papers receiving a total of 363 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Astronomy and Astrophysics, 10 papers in Artificial Intelligence and 3 papers in Instrumentation. Recurrent topics in Miles Cranmer's work include Galaxies: Formation, Evolution, Phenomena (9 papers), Gamma-ray bursts and supernovae (8 papers) and Stellar, planetary, and galactic studies (6 papers). Miles Cranmer is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (9 papers), Gamma-ray bursts and supernovae (8 papers) and Stellar, planetary, and galactic studies (6 papers). Miles Cranmer collaborates with scholars based in United States, United Kingdom and Canada. Miles Cranmer's co-authors include Shirley Ho, Daniel Tamayo, Peter Battaglia, Pablo Lemos, David N. Spergel, N Jeffrey, Daniel Anglés‐Alcázar, Leander Thiele, V. Ashley Villar and E. Berger and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and The Astrophysical Journal.

In The Last Decade

Miles Cranmer

21 papers receiving 319 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Miles Cranmer United States 12 209 111 55 44 36 23 363
Daniel George United States 5 416 2.0× 145 1.3× 41 0.7× 14 0.3× 28 0.8× 8 533
N Jeffrey United Kingdom 9 156 0.7× 73 0.7× 31 0.6× 38 0.9× 28 0.8× 15 260
José Manuel Zorrilla Matilla United States 9 318 1.5× 62 0.6× 116 2.1× 61 1.4× 23 0.6× 10 368
Janis Fluri Switzerland 9 288 1.4× 62 0.6× 89 1.6× 51 1.2× 22 0.6× 13 346
Jessi Cisewski-Kehe United States 10 165 0.8× 52 0.5× 16 0.3× 52 1.2× 27 0.8× 33 368
Tom Charnock France 10 464 2.2× 120 1.1× 211 3.8× 72 1.6× 14 0.4× 15 566
Nikolaos Karnesis Greece 15 595 2.8× 67 0.6× 124 2.3× 36 0.8× 44 1.2× 35 688
Marco Selig Germany 9 212 1.0× 31 0.3× 139 2.5× 14 0.3× 16 0.4× 20 343
Michelle Lochner South Africa 11 241 1.2× 50 0.5× 79 1.4× 56 1.3× 11 0.3× 21 336
Christopher Bonnett Spain 7 294 1.4× 44 0.4× 25 0.5× 157 3.6× 34 0.9× 8 341

Countries citing papers authored by Miles Cranmer

Since Specialization
Citations

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

Fields of papers citing papers by Miles Cranmer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Miles Cranmer

This figure shows the co-authorship network connecting the top 25 collaborators of Miles Cranmer. A scholar is included among the top collaborators of Miles Cranmer 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 Miles Cranmer. Miles Cranmer 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.
Bomarito, Geoffrey, et al.. (2025). Call for Action: towards the next generation of symbolic regression benchmark. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2529–2538.
2.
Tsoi, Ho Fung, Dylan Rankin, C. Caillol, et al.. (2025). SymbolFit: Automatic Parametric Modeling with Symbolic Regression. CERN Document Server (European Organization for Nuclear Research). 9(1). 2 indexed citations
3.
Parker, Liam, François Lanusse, Siavash Golkar, et al.. (2024). AstroCLIP: a cross-modal foundation model for galaxies. Monthly Notices of the Royal Astronomical Society. 531(4). 4990–5011. 19 indexed citations
4.
Cranmer, Miles, et al.. (2024). Accelerating Giant-impact Simulations with Machine Learning. The Astrophysical Journal. 975(2). 228–228. 1 indexed citations
5.
Tsoi, Ho Fung, Vladimir Lončar, Ekaterina Govorkova, et al.. (2024). Symbolic Regression on FPGAs for Fast Machine Learning Inference. SHILAP Revista de lepidopterología. 295. 9036–9036. 9 indexed citations
6.
Belokurov, Vasily, et al.. (2024). The ones that got away: chemical tagging of globular cluster-origin stars with Gaia BP/RP spectra. Monthly Notices of the Royal Astronomical Society. 536(3). 2507–2524. 3 indexed citations
7.
Wadekar, Digvijay, Leander Thiele, J. Colin Hill, et al.. (2023). The SZ flux-mass (Y–M) relation at low-halo masses: improvements with symbolic regression and strong constraints on baryonic feedback. Monthly Notices of the Royal Astronomical Society. 522(2). 2628–2643. 22 indexed citations
8.
Lemos, Pablo, Miles Cranmer, ChangHoon Hahn, et al.. (2023). Robust simulation-based inference in cosmology with Bayesian neural networks. Machine Learning Science and Technology. 4(1). 01LT01–01LT01. 21 indexed citations
9.
Lemos, Pablo, N Jeffrey, Miles Cranmer, Shirley Ho, & Peter Battaglia. (2023). Rediscovering orbital mechanics with machine learning. Machine Learning Science and Technology. 4(4). 45002–45002. 41 indexed citations
10.
Birrer, Simon, et al.. (2023). Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks. The Astrophysical Journal. 953(2). 178–178. 3 indexed citations
11.
Wadekar, Digvijay, Leander Thiele, Francisco Villaescusa-Navarro, et al.. (2023). Augmenting astrophysical scaling relations with machine learning: Application to reducing the Sunyaev–Zeldovich flux–mass scatter. Proceedings of the National Academy of Sciences. 120(12). e2202074120–e2202074120. 19 indexed citations
12.
Urry, C. M., Laurence Perreault-Levasseur, Miles Cranmer, et al.. (2022). GaMPEN: A Machine-learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters. The Astrophysical Journal. 935(2). 138–138. 9 indexed citations
13.
Thiele, Leander, Miles Cranmer, William R. Coulton, Shirley Ho, & David N. Spergel. (2022). Predicting the thermal Sunyaev–Zel’dovich field using modular and equivariant set-based neural networks. Machine Learning Science and Technology. 3(3). 35002–35002. 4 indexed citations
14.
Belokurov, Vasily, et al.. (2022). Charting Galactic Accelerations with Stellar Streams and Machine Learning. The Astrophysical Journal. 940(1). 22–22. 11 indexed citations
15.
Villar, V. Ashley, Miles Cranmer, E. Berger, et al.. (2021). A Deep-learning Approach for Live Anomaly Detection of Extragalactic Transients. The Astrophysical Journal Supplement Series. 255(2). 24–24. 33 indexed citations
16.
Cranmer, Miles & Daniel Tamayo. (2021). Dataset for "A Bayesian neural network predicts the dissolution of compact planetary systems". Zenodo (CERN European Organization for Nuclear Research). 17 indexed citations
17.
Tamayo, Daniel, Miles Cranmer, Sam Hadden, et al.. (2020). Predicting the long-term stability of compact multiplanet systems. Proceedings of the National Academy of Sciences. 117(31). 18194–18205. 53 indexed citations
18.
Cranmer, Miles, Álvaro Sánchez‐González, Peter Battaglia, et al.. (2020). Discovering Symbolic Models from Deep Learning with Inductive Biases. Neural Information Processing Systems. 33. 17429–17442. 1 indexed citations
19.
Cranmer, Miles, Benjamin R. Barsdell, Danny C. Price, et al.. (2017). Bifrost: A Python/C++ Framework for High-Throughput Stream Processing in Astronomy. Journal of Astronomical Instrumentation. 6(4). 27 indexed citations
20.
Bourgoin, Jean‐Philippe, et al.. (2015). Free-space quantum key distribution to a moving receiver. arXiv (Cornell University). 33 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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