Jonathan Niles‐Weed
Impact in
- Space and Planetary Science top 10%
- Archaeological Research and Protection
- Statistics and Probability top 5%
- Markov Chains and Monte Carlo Methods
- Statistical Methods and Inference
Papers in
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- Point processes and geometric inequalities 7
- Geometric Analysis and Curvature Flows 4
- Nonlinear Partial Differential Equations 3
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- Markov Chains and Monte Carlo Methods 3
- Random Matrices and Applications 3
- Statistical Methods and Inference 2
- Co-authors
- Philippe Rigollet (2 shared papers)Jason M. Altschuler (1 shared paper)Quentin Berthet (1 shared paper)Eustasio del Barrio (1 shared paper)Sivaraman Balakrishnan (1 shared paper)Larry Wasserman (1 shared paper)Jean–Michel Loubes (1 shared paper)Carlos Fernandez‐Granda (1 shared paper)
- Journals
- The Annals of Statistics (3 papers)Lecture notes in mathematics (1 paper)Scientific Reports (1 paper)Foundations of Computational Mathematics (1 paper)SIAM Journal on Mathematical Analysis (1 paper)
- Partner nations
- United StatesFranceSwitzerland
In The Last Decade
Jonathan Niles‐Weed
16 papers receiving 167 citations
Peers
Comparison fields: 5 of 66
- Space and Planetary Science 15
- Statistics and Probability 64
- Applied Mathematics 54
- Structural Biology 6
- Mathematical Physics 20
Countries citing papers authored by Jonathan Niles‐Weed
This map shows the geographic impact of Jonathan Niles‐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 Niles‐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 Niles‐Weed more than expected).
Fields of papers citing papers by Jonathan Niles‐Weed
This network shows the impact of papers produced by Jonathan Niles‐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 Niles‐Weed. The network helps show where Jonathan Niles‐Weed may publish in the future.
Co-authors
The 25 scholars most cited alongside Jonathan Niles‐Weed, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 26 | |
| 2 | 2021 | 21 | |
| 3 | 2023 | 17 | |
| 4 | 2022 | 17 | |
| 5 | 2024 | 16 | |
| 6 | 2023 | 15 | |
| 7 | Early-Learning Regularization Prevents Memorization of Noisy Labels | 2020 | 13 |
| 8 | 2024 | 12 | |
| 9 | 2021 | 9 | |
| 10 | 2022 | 9 | |
| 11 | 2021 | 7 | |
| 12 | 2021 | 5 | |
| 13 | 2023 | 4 | |
| 14 | 2025 | 2 | |
| 15 | The All-or-Nothing Phenomenon in Sparse Tensor PCA | 2020 | 1 |
| 16 | 2025 | 1 | |
| 17 | 2025 | 0 |
About Jonathan Niles‐Weed
Jonathan Niles‐Weed is a scholar working on Applied Mathematics, Statistics and Probability, Computer Vision and Pattern Recognition, Mathematical Physics and Space and Planetary Science, having authored 17 papers that have together received 175 indexed citations. Recurring topics across this work include Point processes and geometric inequalities (7 papers), Geometric Analysis and Curvature Flows (4 papers), Nonlinear Partial Differential Equations (3 papers), Markov Chains and Monte Carlo Methods (3 papers), Random Matrices and Applications (3 papers), Statistical Methods and Inference (2 papers), Archaeological Research and Protection (2 papers) and Advanced Neuroimaging Techniques and Applications (1 paper). The work is most often cited by research in Space and Planetary Science (15 citations), Statistics and Probability (64 citations), Applied Mathematics (54 citations), Structural Biology (6 citations) and Mathematical Physics (20 citations). Jonathan Niles‐Weed has collaborated with scholars based in United States, France and Switzerland. Frequent co-authors include Philippe Rigollet, Jason M. Altschuler, Quentin Berthet, Eustasio del Barrio, Sivaraman Balakrishnan, Larry Wasserman, Jean–Michel Loubes, Carlos Fernandez‐Granda, Sheng Liu and Afonso S. Bandeira. Their work appears in journals such as The Annals of Statistics, Lecture notes in mathematics, Scientific Reports, Foundations of Computational Mathematics and SIAM Journal on Mathematical Analysis.
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.