Sarah Tan

640 total citations
11 papers, 120 citations indexed

About

Sarah Tan is a scholar working on Artificial Intelligence, Statistics and Probability and Biomedical Engineering. According to data from OpenAlex, Sarah Tan has authored 11 papers receiving a total of 120 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 2 papers in Statistics and Probability and 2 papers in Biomedical Engineering. Recurrent topics in Sarah Tan's work include Explainable Artificial Intelligence (XAI) (8 papers), Machine Learning and Data Classification (7 papers) and Adversarial Robustness in Machine Learning (6 papers). Sarah Tan is often cited by papers focused on Explainable Artificial Intelligence (XAI) (8 papers), Machine Learning and Data Classification (7 papers) and Adversarial Robustness in Machine Learning (6 papers). Sarah Tan collaborates with scholars based in United States, Canada and Austria. Sarah Tan's co-authors include Rich Caruana, Giles Hooker, Paul Koch, Martin T. Wells, Chun‐Hao Chang, Anna Goldenberg, Yin Lou, Albert Gordo, Urszula Chajewska and Xuezhou Zhang and has published in prestigious journals such as Machine Learning, Electrophoresis and arXiv (Cornell University).

In The Last Decade

Sarah Tan

9 papers receiving 114 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sarah Tan United States	7	84	18	8	6	6	11	120
Jean-François Rajotte Canada	3	32 0.4×	14 0.8×	5 0.6×	5 0.8×	11 1.8×	6	77
Natalie Dullerud United States	4	69 0.8×	15 0.8×	4 0.5×	4 0.7×	11 1.8×	7	90
Prashant Shah United States	3	58 0.7×	9 0.5×	2 0.3×	2 0.3×	6 1.0×	5	79
Christos K. Aridas Greece	6	60 0.7×	3 0.2×	3 0.4×	3 0.5×	8 1.3×	8	92
Konstantina Palla United Kingdom	5	39 0.5×	10 0.6×	2 0.3×	7 1.2×	9 1.5×	12	79
Hunter Lang United States	3	113 1.3×	35 1.9×	2 0.3×	16 2.7×	8 1.3×	6	150
Rajiv Mathews United States	7	90 1.1×	8 0.4×	2 0.3×	1 0.2×	6 1.0×	13	103
Ayesha Bajwa Hong Kong	3	48 0.6×	7 0.4×	2 0.3×		7 1.2×	4	65
Yuanhan Mo United Kingdom	4	28 0.3×	4 0.2×	16 2.0×	7 1.2×	17 2.8×	7	91
Timo Freiesleben Germany	4	49 0.6×	11 0.6×	3 0.4×	1 0.2×	5 0.8×	5	89

Countries citing papers authored by Sarah Tan

Since Specialization

Citations

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

Fields of papers citing papers by Sarah Tan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sarah Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Sarah Tan. A scholar is included among the top collaborators of Sarah Tan 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 Sarah Tan. Sarah Tan 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:

11 of 11 papers shown

1.

Tan, Sarah, et al.. (2024). S-STEM Student Reflections and IDP Process. 2021 ASEE Virtual Annual Conference Content Access Proceedings.

2.

Tan, Sarah, Giles Hooker, Paul Koch, Albert Gordo, & Rich Caruana. (2023). Considerations when learning additive explanations for black-box models. Machine Learning. 112(9). 3333–3359. 17 indexed citations

3.

Wu, Han, Sarah Tan, Weiwei Li, et al.. (2022). Interpretable Personalized Experimentation. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4173–4183.

4.

Chang, Chun‐Hao, et al.. (2021). How Interpretable and Trustworthy are GAMs?. 95–105. 28 indexed citations

5.

Lengerich, Benjamin J., Sarah Tan, Chun‐Hao Chang, Giles Hooker, & Rich Caruana. (2020). Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. International Conference on Artificial Intelligence and Statistics. 2402–2412. 3 indexed citations

6.

Tan, Sarah, et al.. (2020). Tree Space Prototypes. 23–34. 24 indexed citations

7.

Zhang, Xuezhou, Sarah Tan, Paul Koch, et al.. (2019). Axiomatic Interpretability for Multiclass Additive Models. 226–234. 21 indexed citations

8.

Tan, Sarah, Rich Caruana, Giles Hooker, & Albert Gordo. (2018). Transparent Model Distillation.. arXiv (Cornell University). 6 indexed citations

9.

Tan, Sarah. (2018). Interpretable Approaches to Detect Bias in Black-Box Models. 382–383. 3 indexed citations

10.

Tan, Sarah, Rich Caruana, Giles Hooker, & Yin Lou. (2017). Auditing Black-Box Models Using Transparent Model Distillation With Side Information. arXiv (Cornell University). 7 indexed citations

11.

Wan, Fen, et al.. (2008). Nanostructured copolymer gels for dsDNA separation by CE. Electrophoresis. 29(23). 4704–4713. 11 indexed citations

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.

Explore authors with similar magnitude of impact