Gal Mishne

793 total citations
35 papers, 362 citations indexed

About

Gal Mishne is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Computer Vision and Pattern Recognition. According to data from OpenAlex, Gal Mishne has authored 35 papers receiving a total of 362 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 10 papers in Cognitive Neuroscience and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Gal Mishne's work include Anomaly Detection Techniques and Applications (7 papers), Neural dynamics and brain function (6 papers) and Functional Brain Connectivity Studies (6 papers). Gal Mishne is often cited by papers focused on Anomaly Detection Techniques and Applications (7 papers), Neural dynamics and brain function (6 papers) and Functional Brain Connectivity Studies (6 papers). Gal Mishne collaborates with scholars based in United States, Israel and Germany. Gal Mishne's co-authors include Israel Cohen, Siyuan Gao, Dustin Scheinost, Ronen Talmon, Cameron Martino, Gibraan Rahman, Rob Knight, Antonio González, George Armstrong and Yoshiki Vázquez‐Baeza and has published in prestigious journals such as Neuron, SHILAP Revista de lepidopterología and Nature Neuroscience.

In The Last Decade

Gal Mishne

30 papers receiving 359 citations

Peers

Gal Mishne

CVPR

RNMI

Eva L. Dyer United States

Mario Ferraro Italy

Mikael Vejdemo‐Johansson United States

Benjamin D. Evans United Kingdom

Mario Hlawitschka Germany

António R. C. Paiva United States

Benjamin D. Haeffele United States

Moein Khajehnejad United States

D. R. Myatt United Kingdom

Adam S. Charles United States

Eva L. Dyer United States

Gal Mishne

139 ×1.6

78 ×0.9

93 ×1.3

CVPR

50 ×0.7

74 ×2.3

RNMI

Citations per year, relative to Gal Mishne Gal Mishne (= 1×) peers Eva L. Dyer

Countries citing papers authored by Gal Mishne

Since Specialization

Citations

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

Fields of papers citing papers by Gal Mishne

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gal Mishne

This figure shows the co-authorship network connecting the top 25 collaborators of Gal Mishne. A scholar is included among the top collaborators of Gal Mishne 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 Gal Mishne. Gal Mishne 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

Mishne, Gal, et al.. (2025). On a Generalization of Wasserstein Distance and the Beckmann Problem to Connection Graphs. SIAM Journal on Scientific Computing. 47(5). A2774–A2800.

Broggini, Thomas, Xiang Ji, Rui Liu, et al.. (2024). Long-wavelength traveling waves of vasomotion modulate the perfusion of cortex. Neuron. 112(14). 2349–2367.e8. 16 indexed citations

Gentner, Timothy Q., et al.. (2024). Guiding Brain-to-Vocalization Decoder Design Using Structured Generalization Error. PubMed. 2024. 1–4.

Cloninger, Alexander, et al.. (2024). Random Walks, Conductance, and Resistance for the Connection Graph Laplacian. SIAM Journal on Matrix Analysis and Applications. 45(3). 1541–1572. 2 indexed citations

Benisty, Hadas, Daniel Barson, Andrew H. Moberly, et al.. (2023). Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior. Nature Neuroscience. 27(1). 148–158. 25 indexed citations

Everaert, Jonas, Hadas Benisty, Reuma Gadassi Polack, Jutta Joormann, & Gal Mishne. (2022). Which features of repetitive negative thinking and positive reappraisal predict depression? An in-depth investigation using artificial neural networks with feature selection.. Journal of Psychopathology and Clinical Science. 131(7). 754–768. 6 indexed citations

Armstrong, George, Gibraan Rahman, Cameron Martino, et al.. (2022). Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data. SHILAP Revista de lepidopterología. 2. 821861–821861. 32 indexed citations

Schiller, Jackie, et al.. (2021). Learning Disentangled Behavior Embeddings. Neural Information Processing Systems. 34. 1 indexed citations

Gao, Siyuan, Xinyue Xia, Dustin Scheinost, & Gal Mishne. (2021). Smooth graph learning for functional connectivity estimation. NeuroImage. 239. 118289–118289. 11 indexed citations

10.

Gao, Siyuan, Gal Mishne, & Dustin Scheinost. (2021). Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low‐dimensional space of brain dynamics. Human Brain Mapping. 42(14). 4510–4524. 33 indexed citations

11.

Holtz, Chester, et al.. (2021). Online Adversarial Purification based on Self-supervised Learning. 10 indexed citations

12.

Cloninger, Alexander, et al.. (2021). LDLE: Low Distortion Local Eigenmaps.. PubMed. 22. 1 indexed citations

13.

Cheng, Xiuyuan & Gal Mishne. (2020). Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian. SIAM Journal on Imaging Sciences. 13(2). 1015–1048. 6 indexed citations

14.

Jaffe, Ariel, Yuval Kluger, George C. Linderman, Gal Mishne, & Stefan Steinerberger. (2020). Randomized near-neighbor graphs, giant components and applications in data science. Journal of Applied Probability. 57(2). 458–476. 4 indexed citations

15.

Gigante, Scott, Adam S. Charles, Smita Krishnaswamy, & Gal Mishne. (2019). Visualizing the PHATE of Neural Networks. arXiv (Cornell University). 32. 1840–1851. 3 indexed citations

16.

Mishne, Gal & Adam S. Charles. (2019). Learning Spatially-correlated Temporal Dictionaries for Calcium Imaging. 1065–1069. 2 indexed citations

17.

Cheng, Xiuyuan, Gal Mishne, & Stefan Steinerberger. (2017). The geometry of nodal sets and outlier detection. Journal of Number Theory. 185. 48–64. 5 indexed citations

18.

Mishne, Gal & Israel Cohen. (2017). Iterative diffusion-based anomaly detection. 1682–1686. 3 indexed citations

19.

Mishne, Gal & Israel Cohen. (2014). Multiscale anomaly detection using diffusion maps and saliency score. 2823–2827. 8 indexed citations

20.

Mishne, Gal & Israel Cohen. (2012). Multiscale Anomaly Detection Using Diffusion Maps. IEEE Journal of Selected Topics in Signal Processing. 7(1). 111–123. 35 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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