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.
Deep generative modeling for single-cell transcriptomics
20181.1k citationsRomain Lopez, Jeffrey Regier et al.Nature Methodsprofile →
Joint probabilistic modeling of single-cell multi-omic data with totalVI
2021249 citationsAdam Gayoso, Zoë Steier et al.Nature Methodsprofile →
Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models
2021236 citationsChenling Xu, Romain Lopez et al.Molecular Systems Biologyprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Jeffrey Regier
Since
Specialization
Citations
This map shows the geographic impact of Jeffrey Regier'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 Jeffrey Regier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeffrey Regier more than expected).
This network shows the impact of papers produced by Jeffrey Regier. 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 Jeffrey Regier. The network helps show where Jeffrey Regier may publish in the future.
Co-authorship network of co-authors of Jeffrey Regier
This figure shows the co-authorship network connecting the top 25 collaborators of Jeffrey Regier.
A scholar is included among the top collaborators of Jeffrey Regier 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 Jeffrey Regier. Jeffrey Regier is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gayoso, Adam, Zoë Steier, Romain Lopez, et al.. (2021). Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nature Methods. 18(3). 272–282.249 indexed citations breakdown →
4.
Xu, Chenling, et al.. (2021). Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models. Molecular Systems Biology. 17(1). e9620–e9620.236 indexed citations breakdown →
5.
Liu, Runjing, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, & Jon McAuliffe. (2019). Rao-Blackwellized Stochastic Gradients for Discrete Distributions. International Conference on Machine Learning. 4023–4031.3 indexed citations
Lopez, Romain, Jeffrey Regier, Michael I. Jordan, & Nir Yosef. (2018). Information Constraints on Auto-Encoding Variational Bayes. arXiv (Cornell University). 31. 6114–6125.7 indexed citations
9.
Tripuraneni, Nilesh, Mitchell Stern, Chi Jin, Jeffrey Regier, & Michael I. Jordan. (2018). Stochastic Cubic Regularization for Fast Nonconvex Optimization. Neural Information Processing Systems. 31. 2899–2908.20 indexed citations
10.
Lopez, Romain, Jeffrey Regier, Michael B. Cole, Michael I. Jordan, & Nir Yosef. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods. 15(12). 1053–1058.1089 indexed citations breakdown →
Miller, Andrew C., Albert W. Wu, Jeffrey Regier, et al.. (2015). A Gaussian process model of quasar spectral energy distributions. Neural Information Processing Systems. 28. 2494–2502.1 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.