This map shows the geographic impact of James Hensman'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 James Hensman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Hensman more than expected).
This network shows the impact of papers produced by James Hensman. 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 James Hensman. The network helps show where James Hensman may publish in the future.
Co-authorship network of co-authors of James Hensman
This figure shows the co-authorship network connecting the top 25 collaborators of James Hensman.
A scholar is included among the top collaborators of James Hensman 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 James Hensman. James Hensman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Adam, Vincent, et al.. (2020). Doubly Sparse Variational Gaussian Processes. International Conference on Artificial Intelligence and Statistics. 2874–2884.2 indexed citations
Salimbeni, Hugh, et al.. (2018). Gaussian Process Conditional Density Estimation. Spiral (Imperial College London). 31. 2385–2395.7 indexed citations
7.
Wilk, Mark van der, Matthias Bauer, St. John, & James Hensman. (2018). Learning Invariances using the Marginal Likelihood. MPG.PuRe (Max Planck Society). 31. 9938–9948.6 indexed citations
Hensman, James, Alexander Matthews, Maurizio Filippone, & Zoubin Ghahramani. (2015). MCMC for Variationally Sparse Gaussian Processes. Graduate School and Research Center in Digital Science (EURECOM). 28. 1648–1656.22 indexed citations
13.
Hensman, James, et al.. (2014). {Hybrid Discriminative-Generative Approach with Gaussian Processes}. International Conference on Artificial Intelligence and Statistics. 47–56.2 indexed citations
Durrande, Nicolas, James Hensman, Magnus Rattray, & Neil D. Lawrence. (2013). Gaussian process models for periodicity detection. arXiv (Cornell University).1 indexed citations
Worden, Keith, et al.. (2008). Force characterisation of a laser impulse using differential evolution with a local interaction simulation algorithm. Lancaster EPrints (Lancaster University).2 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.