Scott W. Linderman

3.3k total citations · 2 hit papers
42 papers, 972 citations indexed

About

Scott W. Linderman is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Cellular and Molecular Neuroscience. According to data from OpenAlex, Scott W. Linderman has authored 42 papers receiving a total of 972 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Cognitive Neuroscience, 17 papers in Artificial Intelligence and 11 papers in Cellular and Molecular Neuroscience. Recurrent topics in Scott W. Linderman's work include Neural dynamics and brain function (18 papers), Gaussian Processes and Bayesian Inference (8 papers) and Bayesian Methods and Mixture Models (6 papers). Scott W. Linderman is often cited by papers focused on Neural dynamics and brain function (18 papers), Gaussian Processes and Bayesian Inference (8 papers) and Bayesian Methods and Mixture Models (6 papers). Scott W. Linderman collaborates with scholars based in United States, Singapore and United Kingdom. Scott W. Linderman's co-authors include Ryan P. Adams, Bernardo L. Sabatini, Sandeep Robert Datta, Winthrop F. Gillis, Jeffrey E. Markowitz, Matthew Johnson, Shay Q. Neufeld, Celia Beron, Keiramarie Robertson and Ralph E. Peterson and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Scott W. Linderman

38 papers receiving 956 citations

Hit Papers

align trajectories

What are hit papers?

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.

Spontaneous behaviour is structured by reinforcement without explicit reward

2023 90 citations Jeffrey E. Markowitz, Winthrop F. Gillis et al. Nature profile →
Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

2024 48 citations Caleb Weinreb, Sherry Lin et al. Nature Methods profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Scott W. Linderman United States	17	451	344	159	132	129	42	972
Alexander B. Wiltschko United States	12	433 1.0×	409 1.2×	167 1.1×	140 1.1×	129 1.0×	18	1.3k
Mayank Kabra India	7	356 0.8×	264 0.8×	101 0.6×	70 0.5×	66 0.5×	20	840
Ralph E. Peterson United States	4	384 0.9×	394 1.1×	37 0.2×	92 0.7×	164 1.3×	6	911
Sue Ann Koay United States	10	760 1.7×	618 1.8×	65 0.4×	213 1.6×	70 0.5×	13	1.2k
Yonatan Loewenstein Israel	24	1.3k 2.9×	749 2.2×	194 1.2×	251 1.9×	44 0.3×	65	2.0k
Kanaka Rajan United States	14	1.2k 2.6×	515 1.5×	354 2.2×	130 1.0×	57 0.4×	28	1.6k
Chung‐Chuan Lo Taiwan	21	983 2.2×	472 1.4×	236 1.5×	90 0.7×	33 0.3×	76	2.4k
Dori Derdikman Israel	21	1.7k 3.7×	1.3k 3.7×	81 0.5×	168 1.3×	152 1.2×	35	2.1k
Roman Borisyuk United Kingdom	22	865 1.9×	454 1.3×	193 1.2×	114 0.9×	94 0.7×	87	1.3k
Edward G. Jones United States	14	542 1.2×	435 1.3×	112 0.7×	166 1.3×	33 0.3×	21	1.2k

Countries citing papers authored by Scott W. Linderman

Since Specialization

Citations

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

Fields of papers citing papers by Scott W. Linderman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Scott W. Linderman. 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 Scott W. Linderman. The network helps show where Scott W. Linderman may publish in the future.

Co-authorship network of co-authors of Scott W. Linderman

This figure shows the co-authorship network connecting the top 25 collaborators of Scott W. Linderman. A scholar is included among the top collaborators of Scott W. Linderman 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 Scott W. Linderman. Scott W. Linderman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Weinreb, Caleb, Tim Sainburg, Winthrop F. Gillis, et al.. (2026). Spontaneous behavior is a succession of self-directed tasks. Neuron. 114(5). 922–937.e12.

Linderman, Scott W., et al.. (2025). Dynamax: A Python package for probabilistic state space modeling with JAX. The Journal of Open Source Software. 10(108). 7069–7069. 1 indexed citations

Campbell, Malcolm, Lyle Kingsbury, Momchil S. Tomov, et al.. (2025). Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics. Neuron. 113(20). 3458–3475.e12. 1 indexed citations

Liu, Mengyu, Aditya Nair, Néstor Coria, Scott W. Linderman, & David J. Anderson. (2024). Encoding of female mating dynamics by a hypothalamic line attractor. Nature. 634(8035). 901–909. 10 indexed citations

Weinreb, Caleb, Sherry Lin, Mohammed Abdal Monium Osman, et al.. (2024). Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics. Nature Methods. 21(7). 1329–1339. 48 indexed citations breakdown →

Wang, Zhaoran, et al.. (2023). Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models. Journal of the American Statistical Association. 119(547). 2382–2395. 1 indexed citations

Marshall, Jesse D., et al.. (2021). Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model.. International Conference on Artificial Intelligence and Statistics. 2800–2808. 11 indexed citations

Creamer, Matthew S., et al.. (2021). Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training. eLife. 10. 17 indexed citations

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

10.

Linderman, Scott W., et al.. (2019). Mutually Regressive Point Processes. Neural Information Processing Systems. 32. 5115–5126. 6 indexed citations

11.

Sun, Ruoxi, Scott W. Linderman, Ian Kinsella, & Liam Paninski. (2019). Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models. Neural Information Processing Systems. 32. 10165–10174. 1 indexed citations

12.

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

13.

Johnson, Robert E., Scott W. Linderman, Thomas Panier, et al.. (2019). Probabilistic Models of Larval Zebrafish Behavior Reveal Structure on Many Scales. Current Biology. 30(1). 70–82.e4. 89 indexed citations

14.

Sharma, Anuj Kumar, Robert E. Johnson, Florian Engert, & Scott W. Linderman. (2018). Point process latent variable models of larval zebrafish behavior. Neural Information Processing Systems. 31. 10919–10930. 9 indexed citations

15.

Naesseth, Christian A., Scott W. Linderman, Rajesh Ranganath, & David M. Blei. (2018). Variational Sequential Monte Carlo. International Conference on Artificial Intelligence and Statistics. 84. 968–977. 23 indexed citations

16.

Markowitz, Jeffrey E., Winthrop F. Gillis, Celia Beron, et al.. (2018). The Striatum Organizes 3D Behavior via Moment-to-Moment Action Selection. Cell. 174(1). 44–58.e17. 224 indexed citations

17.

Mena, Gonzalo E., David Belanger, Scott W. Linderman, & Jasper Snoek. (2018). Learning Latent Permutations with Gumbel-Sinkhorn Networks. Oxford University Research Archive (ORA) (University of Oxford). 19 indexed citations

18.

Linderman, Scott W., Ryan P. Adams, & Jonathan W. Pillow. (2016). Bayesian latent structure discovery from multi-neuron recordings. Neural Information Processing Systems. 29. 2002–2010. 9 indexed citations

19.

Naesseth, Christian A., Francisco J. R. Ruiz, Scott W. Linderman, & David M. Blei. (2016). Rejection Sampling Variational Inference. arXiv (Cornell University). 1 indexed citations

20.

Nguyen, Vincent, et al.. (2016). Cross-corpora unsupervised learning of trajectories in autism spectrum disorders. Journal of Machine Learning Research. 17(1). 4597–4634. 3 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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