Gabriel Grand

797 total citations · 1 hit paper
2 papers, 100 citations indexed

About

Gabriel Grand is a scholar working on Artificial Intelligence, Cultural Studies and Computer Science Applications. According to data from OpenAlex, Gabriel Grand has authored 2 papers receiving a total of 100 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Artificial Intelligence, 1 paper in Cultural Studies and 1 paper in Computer Science Applications. Recurrent topics in Gabriel Grand's work include Intelligent Tutoring Systems and Adaptive Learning (1 paper), Natural Language Processing Techniques (1 paper) and Language and cultural evolution (1 paper). Gabriel Grand is often cited by papers focused on Intelligent Tutoring Systems and Adaptive Learning (1 paper), Natural Language Processing Techniques (1 paper) and Language and cultural evolution (1 paper). Gabriel Grand collaborates with scholars based in United States. Gabriel Grand's co-authors include Francisco Pereira, Idan Blank, Evelina Fedorenko, Kevin Ellis, Matthew L. Bowers, Armando Solar-Lezama and Joshua B. Tenenbaum and has published in prestigious journals such as Nature Human Behaviour and Proceedings of the ACM on Programming Languages.

In The Last Decade

Gabriel Grand

2 papers receiving 97 citations

Hit Papers

Semantic projection recovers rich human knowledge of mult... 2022 2026 2023 2024 2022 25 50 75

Peers

Gabriel Grand
Erin Grant United States
Verna Dankers Netherlands
Jon Gauthier United States
Aakriti Kumar United States
Matthijs Westera Netherlands
Caroline Lehr Switzerland
Magdalena Kaufmann United States
Rebecca Marvin United States
Ethan Wilcox United States
Erin Grant United States
Gabriel Grand
Citations per year, relative to Gabriel Grand Gabriel Grand (= 1×) peers Erin Grant

Countries citing papers authored by Gabriel Grand

Since Specialization
Citations

This map shows the geographic impact of Gabriel Grand'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 Gabriel Grand with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriel Grand more than expected).

Fields of papers citing papers by Gabriel Grand

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gabriel Grand. 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 Gabriel Grand. The network helps show where Gabriel Grand may publish in the future.

Co-authorship network of co-authors of Gabriel Grand

This figure shows the co-authorship network connecting the top 25 collaborators of Gabriel Grand. A scholar is included among the top collaborators of Gabriel Grand 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 Gabriel Grand. Gabriel Grand is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

2 of 2 papers shown
1.
Bowers, Matthew L., et al.. (2023). Top-Down Synthesis for Library Learning. Proceedings of the ACM on Programming Languages. 7(POPL). 1182–1213. 15 indexed citations
2.
Grand, Gabriel, Idan Blank, Francisco Pereira, & Evelina Fedorenko. (2022). Semantic projection recovers rich human knowledge of multiple object features from word embeddings. Nature Human Behaviour. 6(7). 975–987. 85 indexed citations breakdown →

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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