Ian Covert

1.4k total citations · 2 hit papers
8 papers, 479 citations indexed

About

Ian Covert is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Ian Covert has authored 8 papers receiving a total of 479 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Cognitive Neuroscience. Recurrent topics in Ian Covert's work include Neural Networks and Applications (3 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Face and Expression Recognition (2 papers). Ian Covert is often cited by papers focused on Neural Networks and Applications (3 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Face and Expression Recognition (2 papers). Ian Covert collaborates with scholars based in United States and United Kingdom. Ian Covert's co-authors include Su‐In Lee, Scott Lundberg, Hugh Chen, Nicholas J. Foti, Ali Shojaie, Emily B. Fox, Rosanne Liu, Ali Farhadi, Sarah I. Pratt and Rohan Gala and has published in prestigious journals such as Nature Communications, IEEE Transactions on Pattern Analysis and Machine Intelligence and Nature Machine Intelligence.

In The Last Decade

Ian Covert

7 papers receiving 469 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.

Algorithms to estimate Shapley value feature attributions

2023 199 citations Hugh Chen, Ian Covert et al. Nature Machine Intelligence profile →
What does a platypus look like? Generating customized prompts for zero-shot image classification

2023 90 citations Sarah I. Pratt, Ian Covert et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ian Covert United States	7	202	72	50	35	33	8	479
Hassan Ramchoun Morocco	5	208 1.0×	65 0.9×	40 0.8×	29 0.8×	22 0.7×	14	548
Youssef Ghanou Morocco	9	278 1.4×	81 1.1×	43 0.9×	33 0.9×	24 0.7×	21	649
Max G’Sell United States	8	214 1.1×	29 0.4×	35 0.7×	35 1.0×	64 1.9×	13	570
I. Cloete South Africa	12	303 1.5×	61 0.8×	49 1.0×	46 1.3×	23 0.7×	37	504
Xuhong Li China	9	221 1.1×	102 1.4×	28 0.6×	14 0.4×	55 1.7×	36	538
Przemysław Szufel Poland	8	156 0.8×	47 0.7×	22 0.4×	53 1.5×	32 1.0×	19	485
Nadia Burkart Germany	4	354 1.8×	40 0.6×	20 0.4×	30 0.9×	18 0.5×	7	593
Cristiano Leite de Castro Brazil	9	330 1.6×	108 1.5×	36 0.7×	19 0.5×	15 0.5×	39	547
Zardad Khan Pakistan	13	183 0.9×	102 1.4×	30 0.6×	43 1.2×	85 2.6×	39	525

Countries citing papers authored by Ian Covert

Since Specialization

Citations

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

Fields of papers citing papers by Ian Covert

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ian Covert

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

8 of 8 papers shown

1.

Covert, Ian, et al.. (2023). Feature Selection in the Contrastive Analysis Setting. 66102–66126.

2.

Covert, Ian, Rohan Gala, Tim Wang, et al.. (2023). Predictive and robust gene selection for spatial transcriptomics. Nature Communications. 14(1). 2091–2091. 11 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.

Pratt, Sarah I., Ian Covert, Rosanne Liu, & Ali Farhadi. (2023). What does a platypus look like? Generating customized prompts for zero-shot image classification. 15645–15655. 90 indexed citations breakdown →

5.

Covert, Ian & Su‐In Lee. (2021). Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression. International Conference on Artificial Intelligence and Statistics. 3457–3465. 30 indexed citations

6.

Covert, Ian, et al.. (2021). Neural Granger Causality. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(8). 1–1. 137 indexed citations

7.

Covert, Ian, Scott Lundberg, & Su‐In Lee. (2020). Understanding Global Feature Contributions With Additive Importance Measures. arXiv (Cornell University). 33. 17212–17223. 6 indexed citations

8.

Covert, Ian, et al.. (2019). Temporal Graph Convolutional Networks for Automatic Seizure Detection. 160–180. 6 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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