Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Neural population dynamics during reaching
2012933 citationsMark M. Churchland, John P. Cunningham et al.profile →
Dimensionality reduction for large-scale neural recordings
2014669 citationsJohn P. Cunningham, Byron M. YuNature Neuroscienceprofile →
Influence of heart rate on the BOLD signal: The cardiac response function
2008505 citationsJohn P. Cunningham et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by John P. Cunningham
Since
Specialization
Citations
This map shows the geographic impact of John P. Cunningham'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 John P. Cunningham with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John P. Cunningham more than expected).
Fields of papers citing papers by John P. Cunningham
This network shows the impact of papers produced by John P. Cunningham. 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 John P. Cunningham. The network helps show where John P. Cunningham may publish in the future.
Co-authorship network of co-authors of John P. Cunningham
This figure shows the co-authorship network connecting the top 25 collaborators of John P. Cunningham.
A scholar is included among the top collaborators of John P. Cunningham 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 John P. Cunningham. John P. Cunningham is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Blei, David M., et al.. (2021). A general linear-time inference method for Gaussian Processes on one dimension. Journal of Machine Learning Research. 22(234). 1–36.1 indexed citations
Wu, Anqi, E. Kelly Buchanan, Matthew R Whiteway, et al.. (2020). Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking. Neural Information Processing Systems. 33. 6040–6052.2 indexed citations
7.
Glaser, Joshua I., Matthew R Whiteway, John P. Cunningham, Liam Paninski, & Scott W. Linderman. (2020). Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations.. Neural Information Processing Systems. 33. 14867–14878.1 indexed citations
8.
Miller, Andrew C., Ziad Obermeyer, John P. Cunningham, & Sendhil Mullainathan. (2019). Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography. International Conference on Machine Learning. 4585–4594.3 indexed citations
9.
Whiteway, Matthew R, Shreya Saxena, Taiga Abe, et al.. (2019). BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. Neural Information Processing Systems. 32. 15680–15691.31 indexed citations
10.
Cunningham, John P., et al.. (2019). The continuous Bernoulli: fixing a pervasive error in variational autoencoders. arXiv (Cornell University). 32. 13266–13276.5 indexed citations
Cunningham, John P., et al.. (2016). Elliptical slice sampling with expectation propagation. Uncertainty in Artificial Intelligence. 172–181.2 indexed citations
Gardner, Jacob R., et al.. (2015). Psychophysical detection testing with Bayesian active learning. Uncertainty in Artificial Intelligence. 286–297.8 indexed citations
15.
Wilson, Andrew Gordon, Elad Gilboa, John P. Cunningham, & Arye Nehorai. (2014). Fast Kernel Learning for Multidimensional Pattern Extrapolation. Neural Information Processing Systems. 27. 3626–3634.42 indexed citations
16.
Gardner, Jacob R., Matt J. Kusner, Zhixiang, Kilian Q. Weinberger, & John P. Cunningham. (2014). Bayesian Optimization with Inequality Constraints. PolyPublie (École Polytechnique de Montréal). 937–945.175 indexed citations
17.
Gilboa, Elad, et al.. (2013). Scaling Multidimensional Gaussian Processes using Projected Additive Approximations. International Conference on Machine Learning. 454–461.14 indexed citations
18.
Cunningham, John P., Zoubin Ghahramani, & Carl Edward Rasmussen. (2012). Gaussian Processes for time-marked time-series data. Cambridge University Engineering Department Publications Database. 255–263.7 indexed citations
19.
Yu, Byron M., John P. Cunningham, Gopal Santhanam, et al.. (2011). Dynamical segmentation of single trials from population neural data. UCL Discovery (University College London). 24. 756–764.34 indexed citations
20.
Macke, Jakob H., Lars Buesing, John P. Cunningham, et al.. (2011). Empirical models of spiking in neural populations. MPG.PuRe (Max Planck Society). 24. 1350–1358.90 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.