Vector Institute

1.3k papers and 36.0k indexed citations

About

In recent decades, authors affiliated with Vector Institute have published 1.3k papers, which have received a total of 36.0k indexed citations. Scholars at this organization have produced 455 papers in Artificial Intelligence, 218 papers in Computer Vision and Pattern Recognition and 129 papers in Materials Chemistry on the topics of Machine Learning in Materials Science (110 papers), Topic Modeling (89 papers) and Artificial Intelligence in Healthcare and Education (69 papers). Their work is cited by papers focused on Artificial Intelligence (10.8k citations), Materials Chemistry (6.0k citations) and Computer Vision and Pattern Recognition (5.7k citations). Authors at Vector Institute collaborate with scholars in Canada, United States and United Kingdom and have published in prestigious journals including Nature, Science and Chemical Reviews. Some of Vector Institute's most productive authors include Alán Aspuru‐Guzik, Benjamín Sánchez-Lengeling, Marzyeh Ghassemi, Bo Wang, Sanja Fidler, Florian Häse, David J. Fleet, Ali Punjani, Andrew L. Beam and Pascal Friederich.

In The Last Decade

Vector Institute

1.1k papers receiving 34.8k citations

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).

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.

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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2026