Jason D. McEwen

2.1k total citations
63 papers, 1.0k citations indexed

About

Jason D. McEwen is a scholar working on Astronomy and Astrophysics, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Jason D. McEwen has authored 63 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Astronomy and Astrophysics, 21 papers in Computer Vision and Pattern Recognition and 15 papers in Computational Mechanics. Recurrent topics in Jason D. McEwen's work include Image and Signal Denoising Methods (19 papers), Radio Astronomy Observations and Technology (17 papers) and Sparse and Compressive Sensing Techniques (14 papers). Jason D. McEwen is often cited by papers focused on Image and Signal Denoising Methods (19 papers), Radio Astronomy Observations and Technology (17 papers) and Sparse and Compressive Sensing Techniques (14 papers). Jason D. McEwen collaborates with scholars based in United Kingdom, Switzerland and United States. Jason D. McEwen's co-authors include Yves Wiaux, Rafael E. Carrillo, Hiranya V. Peiris, Xiaohao Cai, Boris Leistedt, Marcelo Pereyra, Michelle Lochner, A. Lasenby, M. P. Hobson and Jean‐Philippe Thiran and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Computational Physics and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

Jason D. McEwen

61 papers receiving 976 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jason D. McEwen United Kingdom	17	507	285	222	129	122	63	1.0k
Hervé Carfantan France	14	259 0.5×	309 1.1×	149 0.7×	68 0.5×	187 1.5×	56	941
J. Bobin France	18	370 0.7×	604 2.1×	471 2.1×	132 1.0×	174 1.4×	72	1.7k
Yves Wiaux United Kingdom	27	675 1.3×	456 1.6×	515 2.3×	244 1.9×	209 1.7×	110	1.8k
Satyanad Kichenassamy France	18	185 0.4×	615 2.2×	180 0.8×	54 0.4×	130 1.1×	53	1.6k
Christopher A. Metzler United States	17	511 1.0×	449 1.6×	483 2.2×	75 0.6×	120 1.0×	48	1.8k
J. Marsden United States	10	147 0.3×	113 0.4×	250 1.1×	68 0.5×	105 0.9×	13	1.7k
J. G. Ables Australia	13	848 1.7×	127 0.4×	98 0.4×	93 0.7×	520 4.3×	35	1.5k
M. P. Hobson United Kingdom	18	612 1.2×	105 0.4×	37 0.2×	33 0.3×	217 1.8×	47	952
Peter W. Michor Austria	20	454 0.9×	261 0.9×	303 1.4×	16 0.1×	258 2.1×	104	2.8k
J. L. Yen Canada	14	229 0.5×	203 0.7×	118 0.5×	140 1.1×	74 0.6×	50	815

Countries citing papers authored by Jason D. McEwen

Since Specialization

Citations

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

Fields of papers citing papers by Jason D. McEwen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jason D. McEwen. 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 Jason D. McEwen. The network helps show where Jason D. McEwen may publish in the future.

Co-authorship network of co-authors of Jason D. McEwen

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

All Works

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20 of 20 papers shown

Price, Matt A., et al.. (2025). Learned harmonic mean estimation of the Bayesian evidence with normalizing flows. The Open Journal of Astrophysics. 8.

Wu, Kinwah, et al.. (2024). A covariant formulation for cosmological radiative transfer of the 21-cm line. Monthly Notices of the Royal Astronomical Society. 531(1). 434–449. 1 indexed citations

Liaudat, T.I, et al.. (2024). Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging. 3(1). 505–534. 4 indexed citations

Mancini, A. Spurio, et al.. (2023). Bayesian model comparison for simulation-based inference. 2(1). 710–722. 13 indexed citations

McEwen, Jason D., et al.. (2023). Paying attention to astronomical transients: introducing the time-series transformer for photometric classification. 3(1). 209–223. 12 indexed citations

Kitching, T., et al.. (2023). Sparse Bayesian mass-mapping using trans-dimensional MCMC. SHILAP Revista de lepidopterología. 6. 1 indexed citations

Peiris, Hiranya V., et al.. (2023). Impact of Rubin Observatory Cadence Choices on Supernovae Photometric Classification. The Astrophysical Journal Supplement Series. 265(2). 43–43. 3 indexed citations

Price, Matt A., et al.. (2023). Learned Harmonic Mean Estimation of the Marginal Likelihood with Normalizing Flows. 10–10. 1 indexed citations

Cai, Xiaohao, Jason D. McEwen, & Marcelo Pereyra. (2022). Proximal nested sampling for high-dimensional Bayesian model selection. Statistics and Computing. 32(5). 13 indexed citations

10.

Peiris, Hiranya V., et al.. (2021). Considerations for optimizing photometric classification of supernovae from the Rubin Observatory. arXiv (Cornell University). 12 indexed citations

11.

Lochner, Michelle, Bruce A. Bassett, Hiranya V. Peiris, et al.. (2020). Classification of multiwavelength transients with machine learning. Monthly Notices of the Royal Astronomical Society. 502(1). 206–224. 11 indexed citations

12.

Cai, Xiaohao, Luke Pratley, & Jason D. McEwen. (2019). Online radio interferometric imaging: assimilating and discarding visibilities on arrival. Monthly Notices of the Royal Astronomical Society. 485(4). 4559–4572. 6 indexed citations

13.

Price, Matt A., et al.. (2018). Sparse Bayesian mass mapping with uncertainties: hypothesis testing of structure: hypothesis testing of structure. arXiv (Cornell University). 6 indexed citations

14.

Lochner, Michelle, et al.. (2016). PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING. The Astrophysical Journal Supplement Series. 225(2). 31–31. 99 indexed citations

15.

McEwen, Jason D., et al.. (2015). A Novel Sampling Theorem on the Rotation Group. IEEE Signal Processing Letters. 22(12). 2425–2429. 18 indexed citations

16.

McEwen, Jason D., Gilles Puy, Jean‐Philippe Thiran, et al.. (2013). Sparse Image Reconstruction on the Sphere: Implications of a New Sampling Theorem. IEEE Transactions on Image Processing. 22(6). 2275–2285. 13 indexed citations

17.

Kennedy, Rodney A., Parastoo Sadeghi, Zubair Khalid, & Jason D. McEwen. (2013). Classification and construction of closed-form kernels for signal representation on the 2-sphere. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8858. 88580M–88580M. 3 indexed citations

18.

Leistedt, Boris & Jason D. McEwen. (2012). Exact Wavelets on the Ball. IEEE Transactions on Signal Processing. 60(12). 6257–6269. 23 indexed citations

19.

McEwen, Jason D., P. Vielva, Yves Wiaux, et al.. (2007). Cosmological Applications of a Wavelet Analysis on the Sphere. Journal of Fourier Analysis and Applications. 13(4). 495–510. 34 indexed citations

20.

McEwen, Jason D., M. P. Hobson, A. Lasenby, & D. Mortlock. (2004). A 6 sigma detection of non-Gaussianity in the WMAP 1-year data using directional spherical wavelets. arXiv (Cornell University). 3 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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