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.
This map shows the geographic impact of Liam Paninski'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 Liam Paninski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liam Paninski more than expected).
This network shows the impact of papers produced by Liam Paninski. 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 Liam Paninski. The network helps show where Liam Paninski may publish in the future.
Co-authorship network of co-authors of Liam Paninski
This figure shows the co-authorship network connecting the top 25 collaborators of Liam Paninski.
A scholar is included among the top collaborators of Liam Paninski 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 Liam Paninski. Liam Paninski 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
5.
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
6.
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
7.
Shah, Nishal P., Sasidhar Madugula, Paweł Hottowy, et al.. (2019). Efficient characterization of electrically evoked responses for neural interfaces. Neural Information Processing Systems. 32. 14421–14431.2 indexed citations
8.
Giovannucci, Andrea, Johannes Friedrich, Anne K. Churchland, et al.. (2017). OnACID: Online Analysis of Calcium Imaging Data in Real Time. Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory). 30. 2381–2391.4 indexed citations
9.
Sun, Ruoxi, Evan Archer, & Liam Paninski. (2017). Scalable Variational Inference for Super Resolution Microscopy. International Conference on Artificial Intelligence and Statistics. 1057–1065.
10.
Chichilnisky, E. J., et al.. (2017). Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons. Neural Information Processing Systems. 30. 6434–6445.9 indexed citations
Carlson, David, et al.. (2016). Partition functions from Rao-Blackwellized tempered sampling. DukeSpace (Duke University). 6. 2896–2905.3 indexed citations
13.
Paige, Brooks, et al.. (2013). Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. Cambridge University Engineering Department Publications Database. 26. 1304–1312.19 indexed citations
14.
Pnevmatikakis, Eftychios A. & Liam Paninski. (2013). Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions. Neural Information Processing Systems. 26. 1250–1258.24 indexed citations
15.
Wood, Frank, et al.. (2012). Low rank continuous-space graphical models. International Conference on Artificial Intelligence and Statistics. 1064–1072.1 indexed citations
Lewi, Jeremy, Robert J. Butera, & Liam Paninski. (2007). Efficient active learning with generalized linear models. International Conference on Artificial Intelligence and Statistics. 267–274.5 indexed citations
18.
Ahrens, Misha B., Liam Paninski, & Quentin J. M. Huys. (2005). Large-scale biophysical parameter estimation in single neurons via constrained linear regression. UCL Discovery (University College London). 25–32.2 indexed citations
19.
Paninski, Liam. (2004). Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning. Neural Information Processing Systems. 17. 1025–1032.13 indexed citations
20.
Paninski, Liam. (2003). Design of Experiments Via Information Theory. SSRN Electronic Journal. 16. 1319–1326.
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.