Valerie Chen

73 total papers · 1.2k total citations
19 papers, 684 citations indexed

About

Valerie Chen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Valerie Chen has authored 19 papers receiving a total of 684 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Molecular Biology. Recurrent topics in Valerie Chen's work include Explainable Artificial Intelligence (XAI) (6 papers), Machine Learning and Data Classification (4 papers) and Adversarial Robustness in Machine Learning (4 papers). Valerie Chen is often cited by papers focused on Explainable Artificial Intelligence (XAI) (6 papers), Machine Learning and Data Classification (4 papers) and Adversarial Robustness in Machine Learning (4 papers). Valerie Chen collaborates with scholars based in United States, United Kingdom and Canada. Valerie Chen's co-authors include Ameet Talwalkar, Joon Sik Kim, Gregory Plumb, Jeffrey Li, Jennifer Wortman Vaughan, Q. Vera Liao, Gagan Bansal, Jochen Hack, Tongshuang Wu and Jian Ma and has published in prestigious journals such as The EMBO Journal, Nature Methods and Communications of the ACM.

In The Last Decade

Valerie Chen

16 papers receiving 651 citations

Hit Papers

Interpretable Machine Lea... 2021 2026 2022 2024 2021 2022 100 200 300

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Valerie Chen 286 55 54 51 39 19 684
A. Mahapatra 225 0.8× 27 0.5× 37 0.7× 20 0.4× 25 0.6× 16 675
Bastian Bohn 251 0.9× 18 0.3× 63 1.2× 52 1.0× 21 0.5× 8 663
Eduardo Soares 350 1.2× 27 0.5× 44 0.8× 16 0.3× 57 1.5× 30 717
Daniel Jiménez 89 0.3× 30 0.5× 29 0.5× 42 0.8× 20 0.5× 25 556
Elena Hernández-Pereira 237 0.8× 31 0.6× 26 0.5× 96 1.9× 18 0.5× 31 770
Stefan Coors 191 0.7× 10 0.2× 55 1.0× 39 0.8× 28 0.7× 7 712
Anna Saranti 406 1.4× 32 0.6× 53 1.0× 12 0.2× 30 0.8× 27 766
Stefano Marrone 212 0.7× 17 0.3× 34 0.6× 28 0.5× 15 0.4× 52 595
Theresa Ullmann 171 0.6× 10 0.2× 33 0.6× 21 0.4× 30 0.8× 11 624
Khan Muhammad 390 1.4× 49 0.9× 20 0.4× 10 0.2× 36 0.9× 11 774

Countries citing papers authored by Valerie Chen

Since Specialization
Citations

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

Fields of papers citing papers by Valerie Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Valerie Chen

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

All Works

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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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