Rob Ashmore

528 total citations
9 papers, 251 citations indexed

About

Rob Ashmore is a scholar working on Artificial Intelligence, Software and Safety, Risk, Reliability and Quality. According to data from OpenAlex, Rob Ashmore has authored 9 papers receiving a total of 251 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 5 papers in Software and 3 papers in Safety, Risk, Reliability and Quality. Recurrent topics in Rob Ashmore's work include Adversarial Robustness in Machine Learning (6 papers), Anomaly Detection Techniques and Applications (5 papers) and Software Reliability and Analysis Research (3 papers). Rob Ashmore is often cited by papers focused on Adversarial Robustness in Machine Learning (6 papers), Anomaly Detection Techniques and Applications (5 papers) and Software Reliability and Analysis Research (3 papers). Rob Ashmore collaborates with scholars based in United Kingdom. Rob Ashmore's co-authors include Colin Paterson, Radu Călinescu, Matthew Q. Hill, Youcheng Sun, Xiaowei Huang, James J. Sharp, Daniel Kroening, Alec Banks, Andrew Howe and Shamal Faily and has published in prestigious journals such as ACM Computing Surveys, ACM Transactions on Embedded Computing Systems and White Rose Research Online (University of Leeds, The University of Sheffield, University of York).

In The Last Decade

Rob Ashmore

9 papers receiving 244 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Rob Ashmore United Kingdom	6	156	74	37	33	23	9	251
Hirotoshi Yasuoka Japan	6	115 0.7×	23 0.3×	31 0.8×	23 0.7×	14 0.6×	7	182
Falk Howar Germany	10	121 0.8×	138 1.9×	73 2.0×	16 0.5×	18 0.8×	45	260
Houssem Ben Braiek Canada	7	123 0.8×	105 1.4×	106 2.9×	21 0.6×	5 0.2×	14	244
Huáscar Espinoza France	7	52 0.3×	83 1.1×	54 1.5×	8 0.2×	76 3.3×	15	186
Chuanqi Tao China	12	115 0.7×	185 2.5×	249 6.7×	28 0.8×	7 0.3×	53	422
Suzanne M. Mahoney United States	9	169 1.1×	14 0.2×	49 1.3×	12 0.4×	8 0.3×	20	247
Brahim Hamid France	9	100 0.6×	77 1.0×	90 2.4×	5 0.2×	27 1.2×	43	205
Helen Treharne United Kingdom	11	142 0.9×	78 1.1×	84 2.3×	33 1.0×	7 0.3×	45	269
Marcello M. Bersani Italy	11	136 0.9×	92 1.2×	73 2.0×	14 0.4×	14 0.6×	42	266
Hrushikesha Mohanty India	8	73 0.5×	77 1.0×	111 3.0×	60 1.8×	3 0.1×	43	249

Countries citing papers authored by Rob Ashmore

Since Specialization

Citations

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

Fields of papers citing papers by Rob Ashmore

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rob Ashmore

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

9 of 9 papers shown

1.

Ashmore, Rob, et al.. (2022). Programming Language Evaluation Criteria for Safety-Critical Software in the Air Domain. Open Access Institutional Repository at Robert Gordon University (Robert Gordon University). 3 indexed citations

2.

Ashmore, Rob & Alec Banks. (2021). The Utility of Neural Network Test Coverage Measures.. National Conference on Artificial Intelligence. 1 indexed citations

3.

Ashmore, Rob, Radu Călinescu, & Colin Paterson. (2021). Assuring the Machine Learning Lifecycle. ACM Computing Surveys. 54(5). 1–39. 118 indexed citations

4.

Alexander, Rob, Hamid Asgari, Rob Ashmore, et al.. (2020). Safety Assurance Objectives for Autonomous Systems. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 7 indexed citations

5.

Banks, Alec & Rob Ashmore. (2019). Requirements Assurance in Machine Learning.. National Conference on Artificial Intelligence. 6 indexed citations

6.

Sun, Youcheng, Xiaowei Huang, Daniel Kroening, et al.. (2019). Structural Test Coverage Criteria for Deep Neural Networks. ACM Transactions on Embedded Computing Systems. 18(5s). 1–23. 57 indexed citations

7.

Sun, Youcheng, Xiaowei Huang, Daniel Kroening, et al.. (2019). Structural Test Coverage Criteria for Deep Neural Networks. 320–321. 18 indexed citations

8.

Sun, Youcheng, Xiaowei Huang, Daniel Kroening, et al.. (2019). DeepConcolic: Testing and Debugging Deep Neural Networks. 111–114. 40 indexed citations

9.

Ashmore, Rob, et al.. (2018). The State of Solutions for Autonomous Systems Safety. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 1 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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