Jonathan Weed

696 total citations
10 papers, 94 citations indexed

About

Jonathan Weed is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Jonathan Weed has authored 10 papers receiving a total of 94 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Computer Networks and Communications, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Signal Processing. Recurrent topics in Jonathan Weed's work include Advanced Antenna and Metasurface Technologies (2 papers), Geometric Analysis and Curvature Flows (2 papers) and Statistical Methods and Inference (2 papers). Jonathan Weed is often cited by papers focused on Advanced Antenna and Metasurface Technologies (2 papers), Geometric Analysis and Curvature Flows (2 papers) and Statistical Methods and Inference (2 papers). Jonathan Weed collaborates with scholars based in United States, United Kingdom and France. Jonathan Weed's co-authors include Philippe Rigollet, Jason M. Altschuler, Quentin Berthet, Damian Evans, Francis Bach, Vianney Perchet, Geoffrey Schiebinger, Aden Forrow, Mor Nitzan and Kristjan Greenewald and has published in prestigious journals such as PLoS ONE, Journal of Machine Learning Research and Bernoulli.

In The Last Decade

Jonathan Weed

9 papers receiving 88 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jonathan Weed United States	5	24	20	17	15	13	10	94
Augustin-Louis Cauchy	2	11 0.5×	10 0.5×	4 0.2×	10 0.7×		7	57
Matthieu Lerasle France	6	42 1.8×	74 3.7×	18 1.1×	8 0.5×		22	129
Darina Dvinskikh Russia	6	46 1.9×	12 0.6×	5 0.3×	13 0.9×		22	81
Thibaut Le Gouic France	5	27 1.1×	51 2.5×	6 0.4×	72 4.8×		10	138
Piotr Zwiernik United States	7	44 1.8×	43 2.1×	3 0.2×	7 0.5×		19	104
Henry E. Daniels United Kingdom	5	8 0.3×	33 1.6×	9 0.5×	6 0.4×		7	115
Lechao Xiao United States	7	59 2.5×	6 0.3×	24 1.4×	37 2.5×		13	113
François‐Xavier Briol United Kingdom	6	39 1.6×	51 2.5×	3 0.2×	5 0.3×		16	98
Henryk Michalewski Poland	5	47 2.0×	3 0.1×	14 0.8×	7 0.5×		22	103
Hadi Daneshmand Switzerland	7	55 2.3×	7 0.3×	20 1.2×	3 0.2×		8	96

Countries citing papers authored by Jonathan Weed

Since Specialization

Citations

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

Fields of papers citing papers by Jonathan Weed

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonathan Weed

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

All Works

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10 of 10 papers shown

1.

Weed, Jonathan & Quentin Berthet. (2019). Estimation of smooth densities in Wasserstein distance. arXiv (Cornell University). 3118–3119. 16 indexed citations

2.

Forrow, Aden, et al.. (2019). Statistical optimal transport via factored couplings. Oxford University Research Archive (ORA) (University of Oxford). 3 indexed citations

3.

Weed, Jonathan & Francis Bach. (2019). Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance. Bernoulli. 25(4A). 7 indexed citations

4.

Goldfeld, Ziv, Kristjan Greenewald, Jonathan Weed, & Yury Polyanskiy. (2019). Optimality of the Plug-in Estimator for Differential Entropy Estimation under Gaussian Convolutions. 892–896. 4 indexed citations

5.

Weed, Jonathan, et al.. (2018). Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval Angkor, Cambodia. PLoS ONE. 13(11). e0205649–e0205649. 24 indexed citations

6.

Altschuler, Jason M., Jonathan Weed, & Philippe Rigollet. (2017). Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration. DSpace@MIT (Massachusetts Institute of Technology). 30. 1964–1974. 28 indexed citations

7.

Weed, Jonathan, Vianney Perchet, & Philippe Rigollet. (2016). Online learning in repeated auctions. Journal of Machine Learning Research. 49. 1562–1583. 7 indexed citations

8.

Weed, Jonathan, et al.. (2015). ON GALOIS GROUPS OF DEGREE 15 POLYNOMIALS. International Journal of Pure and Apllied Mathematics. 104(3). 2 indexed citations

9.

Weed, Jonathan, et al.. (2005). Modeling triangularly packed array antennas using a hexagonal FFT. 22. 507–510. 3 indexed citations

10.

Weed, Jonathan, et al.. (2005). An efficent implementation of a hexagonal FFT. 9. 488–491.

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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