Ashraf Abdul

1.6k total citations · 1 hit paper
7 papers, 956 citations indexed

About

Ashraf Abdul is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ashraf Abdul has authored 7 papers receiving a total of 956 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Information Systems and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ashraf Abdul's work include Machine Learning in Healthcare (3 papers), Explainable Artificial Intelligence (XAI) (3 papers) and Topic Modeling (3 papers). Ashraf Abdul is often cited by papers focused on Machine Learning in Healthcare (3 papers), Explainable Artificial Intelligence (XAI) (3 papers) and Topic Modeling (3 papers). Ashraf Abdul collaborates with scholars based in Singapore, Denmark and United States. Ashraf Abdul's co-authors include Brian Y. Lim, Danding Wang, Qian Yang, Mohan Kankanhalli, Jo Vermeulen and Wei Tsang Ooi and has published in prestigious journals such as ACM Transactions on Internet Technology and National University of Singapore.

In The Last Decade

Ashraf Abdul

7 papers receiving 917 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.

Trends and Trajectories for Explainable, Accountable and Intelligible Systems

2018 423 citations Ashraf Abdul, Jo Vermeulen et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ashraf Abdul Singapore	6	657	304	173	110	99	7	956
Danding Wang China	8	698 1.1×	290 1.0×	173 1.0×	120 1.1×	151 1.5×	18	1.0k
Stephanie Houde United States	10	384 0.6×	313 1.0×	122 0.7×	72 0.7×	94 0.9×	24	829
Gagan Bansal United States	11	471 0.7×	239 0.8×	111 0.6×	58 0.5×	62 0.6×	23	770
Justin D. Weisz United States	13	397 0.6×	143 0.5×	72 0.4×	102 0.9×	137 1.4×	37	856
Tongshuang Wu United States	17	608 0.9×	93 0.3×	58 0.3×	208 1.9×	133 1.3×	48	971
Kuntal Dey India	15	638 1.0×	311 1.0×	88 0.5×	141 1.3×	175 1.8×	54	1.1k
Emily Reif United States	10	456 0.7×	72 0.2×	91 0.5×	110 1.0×	74 0.7×	17	802
Michael Madaio United States	14	266 0.4×	399 1.3×	87 0.5×	31 0.3×	133 1.3×	32	790
Shivani Kapania United States	8	233 0.4×	172 0.6×	61 0.4×	95 0.9×	77 0.8×	14	610
Harmanpreet Kaur United States	10	230 0.4×	110 0.4×	45 0.3×	52 0.5×	30 0.3×	35	547

Countries citing papers authored by Ashraf Abdul

Since Specialization

Citations

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

Fields of papers citing papers by Ashraf Abdul

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ashraf Abdul

This figure shows the co-authorship network connecting the top 25 collaborators of Ashraf Abdul. A scholar is included among the top collaborators of Ashraf Abdul 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 Ashraf Abdul. Ashraf Abdul 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.

Abdul, Ashraf, et al.. (2023). Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages. 378–380. 6 indexed citations

2.

Abdul, Ashraf, et al.. (2020). COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations. National University of Singapore. 1–14. 45 indexed citations

3.

Lim, Brian Y., Qian Yang, Ashraf Abdul, & Danding Wang. (2019). Why these Explanations? Selecting Intelligibility Types for Explanation Goals.. 16 indexed citations

4.

Abdul, Ashraf, et al.. (2019). CloseUp—A Community-Driven Live Online Search Engine. ACM Transactions on Internet Technology. 19(3). 1–21. 1 indexed citations

5.

Wang, Danding, Qian Yang, Ashraf Abdul, & Brian Y. Lim. (2019). Designing Theory-Driven User-Centric Explainable AI. 1–15. 460 indexed citations

6.

Abdul, Ashraf, Jo Vermeulen, Danding Wang, Brian Y. Lim, & Mohan Kankanhalli. (2018). Trends and Trajectories for Explainable, Accountable and Intelligible Systems. 1–18. 423 indexed citations breakdown →

7.

Abdul, Ashraf, et al.. (2017). Cyber-Physical Social Networks. ACM Transactions on Internet Technology. 17(2). 1–25. 5 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.

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