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.
Countries citing papers authored by William Bialek
Since
Specialization
Citations
This map shows the geographic impact of William Bialek'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 William Bialek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William Bialek more than expected).
This network shows the impact of papers produced by William Bialek. 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 William Bialek. The network helps show where William Bialek may publish in the future.
Co-authorship network of co-authors of William Bialek
This figure shows the co-authorship network connecting the top 25 collaborators of William Bialek.
A scholar is included among the top collaborators of William Bialek 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 William Bialek. William Bialek is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bialek, William, et al.. (2020). Information-bottleneck renormalization group for self-supervised representation learning. Bulletin of the American Physical Society.2 indexed citations
5.
Berman, Gordon J, William Bialek, & Joshua W. Shaevitz. (2016). Predictability and hierarchy in Drosophila behavior. Proceedings of the National Academy of Sciences. 113(42). 11943–11948.119 indexed citations
Berman, Gordon J, Daniel M. Choi, William Bialek, & Joshua W. Shaevitz. (2014). Mapping the stereotyped behaviour of freely moving fruit flies. Journal of The Royal Society Interface. 11(99). 20140672–20140672.308 indexed citations breakdown →
8.
Stephens, Greg J., William S. Ryu, & William Bialek. (2010). The emergence of stereotyped behaviors in {\em C. elegans}. Bulletin of the American Physical Society. 2010.2 indexed citations
Slonim, Noam, Gurinder S. Atwal, Gašper Tkačik, & William Bialek. (2005). Information-based clustering. Proceedings of the National Academy of Sciences. 102(51). 18297–18302.149 indexed citations
12.
Gregor, Thomas, William Bialek, Rob R. de Ruyter van Steveninck, David W. Tank, & Eric Wieschaus. (2005). Diffusion and scaling during early embryonic pattern formation. Proceedings of the National Academy of Sciences. 102(51). 18403–18407.244 indexed citations
Still, Susanne, William Bialek, & Léon Bottou. (2003). Geometric Clustering Using the Information Bottleneck Method. neural information processing systems. 16. 1165–1172.17 indexed citations
Ruderman, Daniel & William Bialek. (1993). Statistics of Natural Images: Scaling in the Woods. neural information processing systems. 6. 551–558.15 indexed citations
Bialek, William, Daniel Ruderman, & A. Zee. (1990). Optimal Sampling of Natural Images: A Design Principle for the Visual System. Neural Information Processing Systems. 3. 363–369.26 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.