P. B. Graff

35.4k total citations
19 papers, 590 citations indexed

About

P. B. Graff is a scholar working on Astronomy and Astrophysics, Artificial Intelligence and Ocean Engineering. According to data from OpenAlex, P. B. Graff has authored 19 papers receiving a total of 590 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Astronomy and Astrophysics, 6 papers in Artificial Intelligence and 2 papers in Ocean Engineering. Recurrent topics in P. B. Graff's work include Pulsars and Gravitational Waves Research (8 papers), Gamma-ray bursts and supernovae (8 papers) and Gaussian Processes and Bayesian Inference (3 papers). P. B. Graff is often cited by papers focused on Pulsars and Gravitational Waves Research (8 papers), Gamma-ray bursts and supernovae (8 papers) and Gaussian Processes and Bayesian Inference (3 papers). P. B. Graff collaborates with scholars based in United States, United Kingdom and Germany. P. B. Graff's co-authors include B. S. Sathyaprakash, Alessandra Buonanno, S. Vitale, J. Veitch, A. Lasenby, Farhan Feroz, M. P. Hobson, V. Raymond, Ilya Mandel and L. P. Singer and has published in prestigious journals such as The Astrophysical Journal, Monthly Notices of the Royal Astronomical Society and The Astrophysical Journal Supplement Series.

In The Last Decade

P. B. Graff

17 papers receiving 571 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
P. B. Graff United States	10	490	197	44	43	28	19	590
L. Guillemot France	16	598 1.2×	257 1.3×	57 1.3×	86 2.0×	19 0.7×	54	647
A. Torres-Forné Spain	16	691 1.4×	224 1.1×	137 3.1×	72 1.7×	43 1.5×	34	777
Jeff Crowder United States	9	662 1.4×	296 1.5×	27 0.6×	104 2.4×	37 1.3×	9	735
M. A. Bizouard France	13	590 1.2×	301 1.5×	97 2.2×	47 1.1×	43 1.5×	33	699
N. Hurley‐Walker Australia	18	900 1.8×	508 2.6×	37 0.8×	54 1.3×	12 0.4×	78	962
M. Hendry United Kingdom	15	885 1.8×	443 2.2×	37 0.8×	78 1.8×	19 0.7×	56	932
Ralph P. Eatough Germany	18	803 1.6×	303 1.5×	81 1.8×	96 2.2×	40 1.4×	39	865
Kaze W. K. Wong United States	17	1.0k 2.1×	374 1.9×	49 1.1×	69 1.6×	38 1.4×	33	1.1k
Lindy Blackburn United States	11	441 0.9×	182 0.9×	73 1.7×	46 1.1×	58 2.1×	33	508
Wenbin Lu United States	21	1.3k 2.7×	386 2.0×	103 2.3×	43 1.0×	12 0.4×	70	1.4k

Countries citing papers authored by P. B. Graff

Since Specialization

Citations

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

Fields of papers citing papers by P. B. Graff

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by P. B. Graff. 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 P. B. Graff. The network helps show where P. B. Graff may publish in the future.

Co-authorship network of co-authors of P. B. Graff

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

All Works

Sort: Min cites: Since: Top N: Style:

19 of 19 papers shown

Scobie, Heather M., Mark J. Panaggio, Alison M. Binder, et al.. (2023). Correlations and Timeliness of COVID-19 Surveillance Data Sources and Indicators ― United States, October 1, 2020–March 22, 2023. MMWR Morbidity and Mortality Weekly Report. 72(19). 529–535. 12 indexed citations

Vitale, S., R. Lynch, V. Raymond, et al.. (2017). Parameter estimation for heavy binary-black holes with networks of second-generation gravitational-wave detectors. Physical review. D. 95(6). 59 indexed citations

Singer, L. P., Hsin-Yu Chen, D. E. Holz, et al.. (2016). GOING THE DISTANCE: MAPPING HOST GALAXIES OF LIGO AND VIRGO SOURCES IN THREE DIMENSIONS USING LOCAL COSMOGRAPHY AND TARGETED FOLLOW-UP. The Astrophysical Journal Letters. 829(1). L15–L15. 105 indexed citations

Singer, L. P., Hsin-Yu Chen, D. E. Holz, et al.. (2016). SUPPLEMENT: “GOING THE DISTANCE: MAPPING HOST GALAXIES OF LIGO AND VIRGO SOURCES IN THREE DIMENSIONS USING LOCAL COSMOGRAPHY AND TARGETED FOLLOW-UP” (2016, ApJL, 829, L15). The Astrophysical Journal Supplement Series. 226(1). 10–10. 27 indexed citations

Jóhannesson, G., Roberto Ruiz de Austri, Aaron C. Vincent, et al.. (2016). BAYESIAN ANALYSIS OF COSMIC RAY PROPAGATION: EVIDENCE AGAINST HOMOGENEOUS DIFFUSION. The Astrophysical Journal. 824(1). 16–16. 103 indexed citations

Graff, P. B., A. Y. Lien, John G. Baker, & T. Sakamoto. (2016). MODELING THE SWIFT BAT TRIGGER ALGORITHM WITH MACHINE LEARNING^*. The Astrophysical Journal. 818(1). 55–55. 5 indexed citations

Farr, B., C. P. L. Berry, Will M. Farr, et al.. (2015). Parameter estimation on gravitational waves from neutron-star binaries with spinning components. DSpace@MIT (Massachusetts Institute of Technology). 2015.

Berry, C. P. L., Ilya Mandel, H. Middleton, et al.. (2015). PARAMETER ESTIMATION FOR BINARY NEUTRON-STAR COALESCENCES WITH REALISTIC NOISE DURING THE ADVANCED LIGO ERA. The Astrophysical Journal. 804(2). 114–114. 89 indexed citations

Graff, P. B., Alessandra Buonanno, & B. S. Sathyaprakash. (2015). Missing Link: Bayesian detection and measurement of intermediate-mass black-hole binaries. Physical review. D. Particles, fields, gravitation, and cosmology. 92(2). 67 indexed citations

10.

Graff, P. B., Farhan Feroz, M. P. Hobson, & A. Lasenby. (2014). SkyNet: an efficient and robust neural network training tool for machine learning in astronomy. Monthly Notices of the Royal Astronomical Society. 441(2). 1741–1759. 55 indexed citations

11.

Hobson, M. P., P. B. Graff, Farhan Feroz, & A. Lasenby. (2014). Machine-learning in astronomy. Proceedings of the International Astronomical Union. 10(S306). 279–287. 3 indexed citations

12.

Graff, P. B. & Farhan Feroz. (2013). SkyNet: Neural network training tool for machine learning in astronomy. ascl.

13.

Graff, P. B., Farhan Feroz, M. P. Hobson, & A. Lasenby. (2013). Neural Networks for Astronomical Data Analysis and Bayesian Inference. 5. 16–23. 6 indexed citations

14.

Graff, P. B., et al.. (2011). BAMBI: blind accelerated multimodal Bayesian inference. arXiv (Cornell University). 1 indexed citations

15.

Graff, P. B., M. P. Hobson, & A. Lasenby. (2011). An investigation into the Multiple Optimised Parameter Estimation and Data compression algorithm. Monthly Notices of the Royal Astronomical Society Letters. 413(1). L66–L70. 9 indexed citations

16.

Feroz, Farhan, Jonathan R. Gair, P. B. Graff, M. P. Hobson, & A. Lasenby. (2010). Classifying LISA gravitational wave burst signals using Bayesian evidence. Classical and Quantum Gravity. 27(7). 75010–75010. 7 indexed citations

17.

Gair, Jonathan R., Farhan Feroz, Stanislav Babak, et al.. (2010). Nested sampling as a tool for LISA data analysis. Journal of Physics Conference Series. 228. 12010–12010. 4 indexed citations

18.

Graff, P. B., Markos Georganopoulos, Eric S. Perlman, & Demosthenes Kazanas. (2008). A Multizone Model for Simulating the High‐Energy Variability of TeV Blazars. The Astrophysical Journal. 689(1). 68–78. 37 indexed citations

19.

Graff, P. B.. (1980). Solar energy in Brazil. 354–361. 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.

Explore authors with similar magnitude of impact