Eva L. Dyer

1.4k total citations
37 papers, 547 citations indexed

About

Eva L. Dyer is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Eva L. Dyer has authored 37 papers receiving a total of 547 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Cognitive Neuroscience, 9 papers in Artificial Intelligence and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Eva L. Dyer's work include Neural dynamics and brain function (11 papers), Cell Image Analysis Techniques (7 papers) and Sparse and Compressive Sensing Techniques (4 papers). Eva L. Dyer is often cited by papers focused on Neural dynamics and brain function (11 papers), Cell Image Analysis Techniques (7 papers) and Sparse and Compressive Sensing Techniques (4 papers). Eva L. Dyer collaborates with scholars based in United States, Canada and China. Eva L. Dyer's co-authors include Richard G. Baraniuk, Mary-Elizabeth Hamstrom, Aswin C. Sankaranarayanan, Narayanan Kasthuri, Doğa Gürsoy, Vincent De Andrade, Lee E. Miller, Konrad P. Körding, Hugo L. Fernandes and Francesco De Carlo and has published in prestigious journals such as Nature, Neuron and Journal of Neuroscience.

In The Last Decade

Eva L. Dyer

32 papers receiving 502 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Eva L. Dyer United States	10	139	93	78	74	74	37	547
Elizabeth Jurrus United States	13	57 0.4×	119 1.3×	41 0.5×	63 0.9×	32 0.4×	27	596
Yanyang Xiao China	6	87 0.6×	88 0.9×	96 1.2×	39 0.5×	23 0.3×	11	438
Cheng Yan China	7	90 0.6×	72 0.8×	63 0.8×	92 1.2×	98 1.3×	20	680
Yilong Liu China	15	57 0.4×	177 1.9×	79 1.0×	29 0.4×	391 5.3×	46	685
Giovanni Naldi Italy	15	274 2.0×	79 0.8×	18 0.2×	287 3.9×	49 0.7×	76	1.2k
Michał Januszewski United States	12	102 0.7×	67 0.7×	33 0.4×	80 1.1×	22 0.3×	23	678
Juan Gao China	14	388 2.8×	117 1.3×	66 0.8×	41 0.6×	114 1.5×	39	762
Reiner Lenz Sweden	16	114 0.8×	537 5.8×	84 1.1×	24 0.3×	51 0.7×	104	1.0k
Hanlin Tang United States	11	213 1.5×	169 1.8×	121 1.6×	35 0.5×	15 0.2×	37	620

Countries citing papers authored by Eva L. Dyer

Since Specialization

Citations

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

Fields of papers citing papers by Eva L. Dyer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eva L. Dyer. 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 Eva L. Dyer. The network helps show where Eva L. Dyer may publish in the future.

Co-authorship network of co-authors of Eva L. Dyer

This figure shows the co-authorship network connecting the top 25 collaborators of Eva L. Dyer. A scholar is included among the top collaborators of Eva L. Dyer 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 Eva L. Dyer. Eva L. Dyer 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

Dyer, Eva L., et al.. (2025). Accepting “the bitter lesson” and embracing the brain’s complexity. 1 indexed citations

Xu, Yifan, et al.. (2024). A nonoscillatory, millisecond-scale embedding of brain state provides insight into behavior. Nature Neuroscience. 27(9). 1829–1843. 4 indexed citations

Wang, Chao, Yuqi Liu, Ralf Weßel, et al.. (2024). Failure in a population: Tauopathy disrupts homeostatic set-points in emergent dynamics despite stability in the constituent neurons. Neuron. 112(21). 3567–3584.e5. 1 indexed citations

Dyer, Eva L., Cole Hurwitz, Liam Paninski, et al.. (2024). Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution. PubMed. 80495–80521. 1 indexed citations

Dyer, Eva L., et al.. (2024). Neurons throughout the brain embed robust signatures of their anatomical location into spike trains. eLife. 13. 1 indexed citations

Bhaskaran‐Nair, Kiran, et al.. (2023). Transcriptomic cell type structures in vivo neuronal activity across multiple timescales. Cell Reports. 42(4). 112318–112318. 6 indexed citations

Bozdag, G. Ozan, Thomas C. Day, Anthony Burnetti, et al.. (2023). De novo evolution of macroscopic multicellularity. Nature. 617(7962). 747–754. 43 indexed citations

Hengen, Keith B., et al.. (2023). Detecting change points in neural population activity with contrastive metric learning. PubMed. 2023. 1–4.

Körding, Konrad P., et al.. (2022). Aligning latent representations of neural activity. Nature Biomedical Engineering. 7(4). 337–343. 8 indexed citations

10.

Dyer, Eva L., et al.. (2020). Multisensory integration in the mouse cortical connectome using a network diffusion model. Network Neuroscience. 4(4). 1030–1054. 9 indexed citations

11.

Johnson, Erik C., Vandana Sampathkumar, Vincent De Andrade, et al.. (2020). A three-dimensional thalamocortical dataset for characterizing brain heterogeneity. Scientific Data. 7(1). 358–358. 11 indexed citations

12.

Rolnick, David & Eva L. Dyer. (2019). Generative models and abstractions for large-scale neuroanatomy datasets. Current Opinion in Neurobiology. 55. 112–120. 6 indexed citations

13.

Dyer, Eva L., et al.. (2019). Brain mapping at high resolutions: Challenges and opportunities. Current Opinion in Biomedical Engineering. 12. 126–131. 7 indexed citations

14.

Kumar, Aditi, Derrick Brittain, Sam Kinn, et al.. (2018). Large-scale neuroanatomy using LASSO: Loop-based Automated Serial Sectioning Operation. PLoS ONE. 13(10). e0206172–e0206172. 7 indexed citations

15.

Pandarinath, Chethan, K. Cora Ames, Abigail A. Russo, et al.. (2018). Latent Factors and Dynamics in Motor Cortex and Their Application to Brain–Machine Interfaces. Journal of Neuroscience. 38(44). 9390–9401. 64 indexed citations

16.

Andrade, Vincent De, William Scullin, Eva L. Dyer, et al.. (2018). Low-dose x-ray tomography through a deep convolutional neural network. Scientific Reports. 8(1). 2575–2575. 68 indexed citations

17.

Dyer, Eva L., William Gray Roncal, Hugo L. Fernandes, et al.. (2017). Quantifying Mesoscale Neuroanatomy Using X-Ray Microtomography. eNeuro. 4(5). ENEURO.0195–17.2017. 61 indexed citations

18.

Dyer, Eva L., Mohammad Gheshlaghi Azar, Matthew G. Perich, et al.. (2017). A cryptography-based approach for movement decoding. Nature Biomedical Engineering. 1(12). 967–976. 31 indexed citations

19.

Dyer, Eva L., Aswin C. Sankaranarayanan, & Richard G. Baraniuk. (2013). Greedy feature selection for subspace clustering. Journal of Machine Learning Research. 14(1). 2487–2517. 94 indexed citations

20.

Majzoobi, Mehrdad, et al.. (2010). Rapid FPGA delay characterization using clock synthesis and sparse sampling. 1–10. 18 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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