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.
Citations per year, relative to Matt J. Kusner Matt J. Kusner (= 1×)
peers
Jianguo Xiao
Countries citing papers authored by Matt J. Kusner
Since
Specialization
Citations
This map shows the geographic impact of Matt J. Kusner'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 Matt J. Kusner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matt J. Kusner more than expected).
This network shows the impact of papers produced by Matt J. Kusner. 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 Matt J. Kusner. The network helps show where Matt J. Kusner may publish in the future.
Co-authorship network of co-authors of Matt J. Kusner
This figure shows the co-authorship network connecting the top 25 collaborators of Matt J. Kusner.
A scholar is included among the top collaborators of Matt J. Kusner 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 Matt J. Kusner. Matt J. Kusner is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kusner, Matt J., et al.. (2021). MPC-friendly commitments for publicly verifiable covert security. Oxford University Research Archive (ORA) (University of Oxford).
Kusner, Matt J., Chris Russell, Joshua R. Loftus, & Ricardo Silva. (2019). Making Decisions that Reduce Discriminatory Impacts. UCL Discovery (University College London). 3591–3600.4 indexed citations
8.
Bradshaw, John, Matt J. Kusner, Brooks Paige, Marwin Segler, & José Miguel Hernández-Lobato. (2019). Generating molecules via chemical reactions. UCL Discovery (University College London).1 indexed citations
9.
Bradshaw, John, Brooks Paige, Matt J. Kusner, Marwin Segler, & José Miguel Hernández-Lobato. (2019). A Model to Search for Synthesizable Molecules. PolyPublie (École Polytechnique de Montréal). 32. 7905–7917.26 indexed citations
10.
Kusner, Matt J., et al.. (2018). TAPAS : tricks to accelerate (encrypted) prediction as a service. Warwick Research Archive Portal (University of Warwick). 4490–4499.8 indexed citations
11.
Russell, Chris, Matt J. Kusner, Joshua R. Loftus, & Ricardo Silva. (2017). When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness. Oxford University Research Archive (ORA) (University of Oxford). 30. 6414–6423.53 indexed citations
Kusner, Matt J., et al.. (2015). Fast distributed k -center clustering with outliers on massive data. PolyPublie (École Polytechnique de Montréal). 28. 1063–1071.20 indexed citations
16.
Gardner, Jacob R., Matt J. Kusner, Zhixiang, Kilian Q. Weinberger, & John P. Cunningham. (2014). Bayesian Optimization with Inequality Constraints. PolyPublie (École Polytechnique de Montréal). 937–945.175 indexed citations
17.
Kusner, Matt J., Stephen Tyree, Kilian Q. Weinberger, & Kunal Agrawal. (2014). Stochastic Neighbor Compression. PolyPublie (École Polytechnique de Montréal). 622–630.30 indexed citations
Xu, Zhixiang, Matt J. Kusner, Gao Huang, & Kilian Q. Weinberger. (2013). Anytime Representation Learning. PolyPublie (École Polytechnique de Montréal). 1076–1084.6 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.