Vector Institute

1.1k papers and 30.6k indexed citations i.

About

In recent decades, authors affiliated with Vector Institute have published 1.1k papers, which have received a total of 30.6k indexed citations. Scholars at this organization have produced 404 papers in Artificial Intelligence, 213 papers in Computer Vision and Pattern Recognition and 116 papers in Materials Chemistry on the topics of Machine Learning in Materials Science (98 papers), Topic Modeling (75 papers) and Multimodal Machine Learning Applications (56 papers). Their work is cited by papers focused on Artificial Intelligence (10.7k citations), Materials Chemistry (5.5k citations) and Computer Vision and Pattern Recognition (5.2k citations). Authors at Vector Institute collaborate with scholars in Canada, United States and Germany and have published in prestigious journals including Nature, Science and Proceedings of the National Academy of Sciences. Some of Vector Institute's most productive authors include Alán Aspuru‐Guzik, Benjamín Sánchez-Lengeling, Marzyeh Ghassemi, Sanja Fidler, Juan Carrasquilla, Florian Häse, David J. Fleet, Ali Punjani, Andrew L. Beam and David Duvenaud.

In The Last Decade

Fields of papers published by authors at Vector Institute

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers affiliated with Vector Institute at the time of their publication. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers affiliated with Vector Institute at the time of their publication.

Countries citing scholars working at Vector Institute

Since Specialization
Citations

This map shows the geographic impact of research produced by authors working at Vector Institute. 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 papers produced at Vector Institute with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vector Institute more than expected).

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.

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2025