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 Stuart Russell
Since
Specialization
Citations
This map shows the geographic impact of Stuart Russell'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 Stuart Russell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stuart Russell more than expected).
This network shows the impact of papers produced by Stuart Russell. 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 Stuart Russell. The network helps show where Stuart Russell may publish in the future.
Co-authorship network of co-authors of Stuart Russell
This figure shows the co-authorship network connecting the top 25 collaborators of Stuart Russell.
A scholar is included among the top collaborators of Stuart Russell 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 Stuart Russell. Stuart Russell is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Moore, David A. & Stuart Russell. (2014). Fast Gaussian process posteriors with product trees. Uncertainty in Artificial Intelligence. 613–622.1 indexed citations
5.
Rogers, Mark C., Lei Li, & Stuart Russell. (2013). Multilinear Dynamical Systems for Tensor Time Series. Neural Information Processing Systems. 26. 2634–2642.42 indexed citations
6.
Russell, Stuart, et al.. (2010). Global seismic monitoring as probabilistic inference. UC Berkeley. 23. 73–81.7 indexed citations
7.
Chatterjee, Shaunak & Stuart Russell. (2010). Why are DBNs sparse. International Conference on Artificial Intelligence and Statistics. 81–88.4 indexed citations
8.
Marthi, Bhaskara, Stuart Russell, & David André. (2006). A compact, hierarchically optimal Q-function decomposition. Uncertainty in Artificial Intelligence. 332–340.3 indexed citations
9.
Milch, Brian, et al.. (2005). Approximate inference for infinite contingent Bayesian networks. International Conference on Artificial Intelligence and Statistics. 238–245.27 indexed citations
Pasula, Hanna, Bhaskara Marthi, Brian Milch, Stuart Russell, & Ilya Shpitser. (2002). Identity Uncertainty and Citation Matching. Neural Information Processing Systems. 15. 1425–1432.176 indexed citations
12.
Freitas, Nando de, et al.. (2001). Variational MCMC. Uncertainty in Artificial Intelligence. 120–127.34 indexed citations
13.
Oza, Nikunj C. & Stuart Russell. (2001). Online Bagging and Boosting. NASA Technical Reports Server (NASA). 229–236.222 indexed citations
14.
Forbes, J. M., et al.. (1997). Feasibility Study of Fully Automated Vehicles Using Decision-theoretic Control. PATH research report.2 indexed citations
Malik, Jitendra & Stuart Russell. (1995). A Machine Vision Based Surveillance System For California Roads. eScholarship (California Digital Library).21 indexed citations
17.
Russell, Stuart, et al.. (1995). Local learning in probabilistic networks with hidden variables. International Joint Conference on Artificial Intelligence. 1146–1152.89 indexed citations
Russell, Stuart, et al.. (1989). Principles of metareasoning. Principles of Knowledge Representation and Reasoning. 400–411.55 indexed citations
20.
Russell, Stuart & Benjamin N. Grosof. (1987). A declarative approach to bias in concept learning. National Conference on Artificial Intelligence. 505–510.24 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.