Liam Paninski
Affiliations: | Columbia University, New York, NY |
Area:
Computation & TheoryWebsite:
http://www.stat.columbia.edu/~liam/Google:
"Liam Paninski"Mean distance: 13.26 (cluster 17) | S | N | B | C | P |
Parents
Sign in to add mentor| John P. Donoghue | research assistant | Brown | ||
| Eero P. Simoncelli | grad student | 2002-2003 | Columbia | |
| (Some rigorous results on the neural coding problem.) | ||||
| Eero P. Simoncelli | post-doc | 2003-2003 | NYU | |
Children
Sign in to add trainee| Shenghao Wu | research assistant | 2016-2018 | |
| Aneesh Kashalikar | research assistant | 2020-2021 | Columbia |
| Timothy A Machado | grad student | Columbia | |
| Estefany Kelly Buchanan | grad student | 2018- | Columbia |
| Sean Escola | grad student | 2008 | Columbia |
| Gary S. Escola | grad student | 2009 | Columbia |
| Kamiar RahnamaRad | grad student | 2006-2011 | Columbia |
| Alexandro D. Ramirez | grad student | 2012 | Columbia |
| Carl Smith | grad student | 2013 | Columbia |
| Maxim Nikitchenko | grad student | 2007-2013 | Columbia |
| David B. Pfau | grad student | 2008-2015 | Columbia |
| Gonzalo E. Mena | grad student | 2012-2017 | Columbia |
| Yashar Ahmadian | post-doc | Columbia | |
| Quentin Huys | post-doc | UCL, Columbia University | |
| Scott W Linderman | post-doc | Columbia | |
| Pengcheng Zhou | post-doc | 2017- | |
| Anqi Wu | post-doc | 2019- | Columbia |
| Eizaburo Doi | post-doc | 2007-2011 | NYU |
| Uygar Sumbul | post-doc | 2014-2017 | Columbia |
| Logan Grosenick | post-doc | 2015-2020 | Columbia |
| Ari Pakman | research scientist |
Collaborators
Sign in to add collaborator| E. J. Chichilnisky | collaborator | NYU | |
| Kianoush Nazarpour | collaborator | 2009- | Columbia |
| Dawen Cai | collaborator | 2015-2016 |
BETA: Related publications
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Publications
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| Zimnik AJ, Ames KC, An X, et al. (2024) Identifying Interpretable Latent Factors with Sparse Component Analysis. Biorxiv : the Preprint Server For Biology |
| Zhang Y, He T, Boussard J, et al. (2023) Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes. Biorxiv : the Preprint Server For Biology |
| Pasarkar A, Kinsella I, Zhou P, et al. (2023) maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data. Biorxiv : the Preprint Server For Biology |
| Ye Z, Shelton AM, Shaker JR, et al. (2023) Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings. Biorxiv : the Preprint Server For Biology |
| Biderman D, Whiteway MR, Hurwitz C, et al. (2023) Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools. Biorxiv : the Preprint Server For Biology |
| Abe T, Kinsella I, Saxena S, et al. (2022) Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis. Neuron |
| Chen S, Loper J, Zhou P, et al. (2022) Blind demixing methods for recovering dense neuronal morphology from barcode imaging data. Plos Computational Biology. 18: e1009991 |
| Turner NL, Macrina T, Bae JA, et al. (2022) Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity. Cell |
| Whiteway MR, Biderman D, Friedman Y, et al. (2021) Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. Plos Computational Biology. 17: e1009439 |
| Kim YJ, Brackbill N, Batty E, et al. (2021) Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings. Neural Computation. 33: 1719-1750 |