Roy R. Lederman

526 total citations
24 papers, 216 citations indexed

About

Roy R. Lederman is a scholar working on Structural Biology, Surfaces, Coatings and Films and Artificial Intelligence. According to data from OpenAlex, Roy R. Lederman has authored 24 papers receiving a total of 216 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Structural Biology, 7 papers in Surfaces, Coatings and Films and 6 papers in Artificial Intelligence. Recurrent topics in Roy R. Lederman's work include Advanced Electron Microscopy Techniques and Applications (9 papers), Electron and X-Ray Spectroscopy Techniques (7 papers) and Neural Networks and Applications (4 papers). Roy R. Lederman is often cited by papers focused on Advanced Electron Microscopy Techniques and Applications (9 papers), Electron and X-Ray Spectroscopy Techniques (7 papers) and Neural Networks and Applications (4 papers). Roy R. Lederman collaborates with scholars based in United States, Israel and Spain. Roy R. Lederman's co-authors include Ronen Talmon, Uri Shaham, Fred J. Sigworth, Vladimir Rokhlin, Joakim Andén, Amit Singer, Jiří Filipovič, Marta Martínez, James Krieger and Muyuan Chen and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Journal of Molecular Biology.

In The Last Decade

Roy R. Lederman

20 papers receiving 210 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Roy R. Lederman United States 9 82 59 53 49 31 24 216
Cecilia Aguerrebere United States 9 87 1.1× 49 0.8× 17 0.3× 58 1.2× 103 3.3× 20 268
JaeHwang Jung South Korea 10 9 0.1× 4 0.1× 23 0.4× 32 0.7× 65 2.1× 21 394
Kyle Kastner United States 4 11 0.1× 2 0.0× 179 3.4× 16 0.3× 48 1.5× 7 305
Martin Paùr Spain 8 6 0.1× 5 0.1× 141 2.7× 4 0.1× 17 0.5× 10 447
Elena Facco Italy 5 6 0.1× 1 0.0× 85 1.6× 94 1.9× 43 1.4× 6 249
Silvia Colabrese Italy 4 89 1.1× 24 0.5× 64 1.3× 12 0.4× 5 245
Yunhui Gao China 11 16 0.2× 5 0.1× 16 0.3× 2 0.0× 101 3.3× 27 362
Di Che Australia 24 5 0.1× 69 1.3× 24 0.5× 17 0.5× 130 1.7k
Jianying Zhou China 11 7 0.1× 15 0.3× 7 0.1× 4 0.1× 9 0.3× 23 318

Countries citing papers authored by Roy R. Lederman

Since Specialization
Citations

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

Fields of papers citing papers by Roy R. Lederman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Roy R. Lederman. 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 Roy R. Lederman. The network helps show where Roy R. Lederman may publish in the future.

Co-authorship network of co-authors of Roy R. Lederman

This figure shows the co-authorship network connecting the top 25 collaborators of Roy R. Lederman. A scholar is included among the top collaborators of Roy R. Lederman 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 Roy R. Lederman. Roy R. Lederman 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.
Brubaker, Marcus A., et al.. (2025). Efficient high-resolution refinement in cryo-EM with stochastic gradient descent. Acta Crystallographica Section D Structural Biology. 81(7). 327–343.
2.
Fan, Zhou, Roy R. Lederman, Yi Sun, Tianhao Wang, & Sheng Xu. (2024). Maximum likelihood for high-noise group orbit estimation and single-particle cryo-EM. The Annals of Statistics. 52(1). 52–77. 1 indexed citations
3.
Lederman, Roy R., et al.. (2024). Multimodal manifold learning using kernel interpolation along geodesic paths. Information Fusion. 114. 102637–102637.
4.
Sigworth, Fred J., et al.. (2023). Methods for Cryo-EM Single Particle Reconstruction of Macromolecules Having Continuous Heterogeneity. Journal of Molecular Biology. 435(9). 168020–168020. 23 indexed citations
5.
Lederman, Roy R., James Krieger, Amaya Jiménez-Moreno, et al.. (2023). Estimating conformational landscapes from Cryo-EM particles by 3D Zernike polynomials. Nature Communications. 14(1). 154–154. 31 indexed citations
6.
Chen, Muyuan, et al.. (2023). Integrating Molecular Models Into CryoEM Heterogeneity Analysis Using Scalable High-resolution Deep Gaussian Mixture Models. Journal of Molecular Biology. 435(9). 168014–168014. 12 indexed citations
7.
Lederman, Roy R., et al.. (2023). On Manifold Learning in Plato’s Cave: Remarks on Manifold Learning and Physical Phenomena. PubMed. 2023. 1–7. 3 indexed citations
8.
Lederman, Roy R., et al.. (2021). Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo. International Conference on Machine Learning. 1072–1081. 1 indexed citations
9.
Lederman, Roy R., James Krieger, Amaya Jiménez-Moreno, et al.. (2021). Approximating deformation fields for the analysis of continuous heterogeneity of biological macromolecules by 3D Zernike polynomials. IUCrJ. 8(6). 992–1005. 12 indexed citations
10.
Lederman, Roy R., James Krieger, David Střelák, et al.. (2021). Continuous heterogeneity analysis of CryoEM images through Zernike polynomials and spherical harmonics. Microscopy and Microanalysis. 27(S1). 1680–1682. 1 indexed citations
11.
Lederman, Roy R., Joakim Andén, & Amit Singer. (2019). Hyper-molecules: on the representation and recovery of dynamical structures for applications in flexible macro-molecules in cryo-EM. Inverse Problems. 36(4). 44005–44005. 15 indexed citations
12.
Stanton, Kelly, Jiaqi Jin, Roy R. Lederman, Sherman M. Weissman, & Yuval Kluger. (2017). Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling. Nucleic Acids Research. 45(21). e173–e173. 5 indexed citations
13.
Shaham, Uri & Roy R. Lederman. (2017). Learning by coincidence: Siamese networks and common variable learning. Pattern Recognition. 74. 52–63. 23 indexed citations
14.
Lederman, Roy R. & Stefan Steinerberger. (2017). Lower Bounds for Truncated Fourier and Laplace Transforms. Integral Equations and Operator Theory. 87(4). 529–543. 1 indexed citations
15.
Lederman, Roy R. & Vladimir Rokhlin. (2016). On the Analytical and Numerical Properties of the Truncated Laplace Transform. Part II. SIAM Journal on Numerical Analysis. 54(2). 665–687. 3 indexed citations
16.
Lederman, Roy R. & Ronen Talmon. (2015). Learning the geometry of common latent variables using alternating-diffusion. Applied and Computational Harmonic Analysis. 44(3). 509–536. 52 indexed citations
17.
Lederman, Roy R., Ronen Talmon, Hau‐Tieng Wu, Yu‐Lun Lo, & Ronald R. Coifman. (2015). Alternating diffusion for common manifold learning with application to sleep stage assessment. 5758–5762. 13 indexed citations
18.
Lederman, Roy R. & Vladimir Rokhlin. (2015). On the Analytical and Numerical Properties of the Truncated Laplace Transform I.. SIAM Journal on Numerical Analysis. 53(3). 1214–1235. 8 indexed citations
19.
Lederman, Roy R.. (2013). A random-permutations-based approach to fast read alignment. BMC Bioinformatics. 14(S5). S8–S8. 8 indexed citations
20.
Lederman, Roy R., et al.. (1986). The interaction of a planar Co(II) complex with dioxygen. Inorganica Chimica Acta. 117(1). 65–68. 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.

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