Hugh Chen

9.5k total citations · 2 hit papers
7 papers, 5.2k citations indexed

About

Hugh Chen is a scholar working on Artificial Intelligence, Molecular Biology and Surgery. According to data from OpenAlex, Hugh Chen has authored 7 papers receiving a total of 5.2k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Molecular Biology and 1 paper in Surgery. Recurrent topics in Hugh Chen's work include Explainable Artificial Intelligence (XAI) (4 papers), Machine Learning in Healthcare (3 papers) and Bayesian Modeling and Causal Inference (2 papers). Hugh Chen is often cited by papers focused on Explainable Artificial Intelligence (XAI) (4 papers), Machine Learning in Healthcare (3 papers) and Bayesian Modeling and Causal Inference (2 papers). Hugh Chen collaborates with scholars based in United States and United Kingdom. Hugh Chen's co-authors include Su‐In Lee, Scott Lundberg, Gabriel Erion, Jordan M. Prutkin, Bala G. Nair, Nisha Bansal, Ronit Katz, Jonathan Himmelfarb, Alex J. DeGrave and Ian Covert and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Nature Biomedical Engineering.

In The Last Decade

Hugh Chen

7 papers receiving 5.1k citations

Hit Papers

align trajectories

What are hit papers?

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.

From local explanations to global understanding with explainable AI for trees

2020 4.8k citations Scott Lundberg, Gabriel Erion et al. Nature Machine Intelligence profile →
Algorithms to estimate Shapley value feature attributions

2023 199 citations Hugh Chen, Ian Covert et al. Nature Machine Intelligence profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Hugh Chen United States	7	1.2k	508	445	422	349	7	5.2k
Gabriel Erion United States	5	1.2k 0.9×	454 0.9×	416 0.9×	406 1.0×	317 0.9×	5	4.8k
Alex J. DeGrave United States	9	1.3k 1.0×	568 1.1×	416 0.9×	407 1.0×	349 1.0×	10	5.2k
Bala G. Nair United States	23	1.5k 1.2×	576 1.1×	482 1.1×	434 1.0×	382 1.1×	63	7.1k
Scott Lundberg United States	14	1.7k 1.4×	703 1.4×	526 1.2×	457 1.1×	420 1.2×	25	6.9k
Jordan M. Prutkin United States	28	1.1k 0.9×	488 1.0×	415 0.9×	407 1.0×	317 0.9×	82	7.5k
Qiwei Ye China	5	1.8k 1.5×	529 1.0×	603 1.4×	409 1.0×	277 0.8×	8	6.7k
Nisha Bansal United States	38	1.1k 0.9×	892 1.8×	415 0.9×	406 1.0×	318 0.9×	164	9.0k
Weidong Ma China	13	1.9k 1.5×	529 1.0×	633 1.4×	461 1.1×	207 0.6×	50	6.9k
Qi Meng China	9	1.9k 1.6×	533 1.0×	611 1.4×	413 1.0×	178 0.5×	21	6.7k
Guolin Ke China	13	2.5k 2.0×	613 1.2×	624 1.4×	428 1.0×	307 0.9×	22	8.0k

Countries citing papers authored by Hugh Chen

Since Specialization

Citations

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

Fields of papers citing papers by Hugh Chen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hugh Chen

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

All Works

Sort: Min cites: Since: Top N: Style:

7 of 7 papers shown

1.

Qiu, Wei, Hugh Chen, Matt Kaeberlein, & Su‐In Lee. (2023). ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age. The Lancet Healthy Longevity. 4(12). e711–e723. 32 indexed citations

2.

Janizek, Joseph D., Safiye Çelik, Hugh Chen, et al.. (2023). Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models. Nature Biomedical Engineering. 7(6). 811–829. 38 indexed citations

3.

Chen, Hugh, Ian Covert, Scott Lundberg, & Su‐In Lee. (2023). Algorithms to estimate Shapley value feature attributions. Nature Machine Intelligence. 5(6). 590–601. 199 indexed citations breakdown →

4.

Chen, Hugh, Scott Lundberg, & Su‐In Lee. (2022). Explaining a series of models by propagating Shapley values. Nature Communications. 13(1). 4512–4512. 128 indexed citations

5.

Qiu, Wei, et al.. (2022). Interpretable machine learning prediction of all-cause mortality. SHILAP Revista de lepidopterología. 2(1). 125–125. 53 indexed citations

6.

Chen, Hugh, et al.. (2021). Forecasting adverse surgical events using self-supervised transfer learning for physiological signals. npj Digital Medicine. 4(1). 167–167. 33 indexed citations

7.

Lundberg, Scott, Gabriel Erion, Hugh Chen, et al.. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence. 2(1). 56–67. 4764 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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