Rob Ashmore

537 citations
10 papers · 308 · h-index 7

Impact in

Papers in

    • Adversarial Robustness in Machine Learning 7
    • Anomaly Detection Techniques and Applications 5
    • Machine Learning and Data Classification 2
    • Software Reliability and Analysis Research 3
    • Software Testing and Debugging Techniques 3

Rob Ashmore

10 papers receiving 297 citations

Peers

Rob Ashmore
Comparison fields: 5 of 53
  • Software 93
  • Health Informatics 11
  • Artificial Intelligence 196
  • Safety, Risk, Reliability and Quality 31
  • Safety Research 27
Replace Hirotoshi Yasuoka with:
Hirotoshi Yasuoka Japan
Houssem Ben Braiek Canada
Ramin Tavakoli Kolagari Germany
Dina Hadžiosmanović Netherlands
Alvaro Miyazawa United Kingdom
Shinpei Ogata Japan
Leonardo Montecchi Italy
Nicholas Matragkas United Kingdom
David Servat France
Jirayus Jiarpakdee Australia
Rob Ashmore relative to Hirotoshi Yasuoka Japan Hirotoshi Yasuoka's profile →
Citations per field
00.5×3.9×
Hirotoshi Yasuoka · 1×
Citations per year

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-authors

The 15 scholars most cited alongside Rob Ashmore, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Rob Ashmore Line = papers co-authored together Rob Ashmore links everyone, so they are left out of the graph.

All Works

10 of 10 papers shown
#Work
1 2021141
2 201969
3 201945
4 201922
5
Safety Assurance Objectives for Autonomous Systems
202010
6 20189
7
Requirements Assurance in Machine Learning.
20197
8 20223
9
The Utility of Neural Network Test Coverage Measures.
20211
10
The State of Solutions for Autonomous Systems Safety
20181

About Rob Ashmore

Rob Ashmore is a scholar working on Artificial Intelligence, Software, Safety, Risk, Reliability and Quality, Radiological and Ultrasound Technology and Statistics, Probability and Uncertainty, having authored 10 papers that have together received 308 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (7 papers), Anomaly Detection Techniques and Applications (5 papers), Software Reliability and Analysis Research (3 papers), Software Testing and Debugging Techniques (3 papers), Safety Systems Engineering in Autonomy (3 papers), Risk and Safety Analysis (2 papers), Occupational Health and Safety Research (2 papers) and Machine Learning and Data Classification (2 papers). The work is most often cited by research in Software (93 citations), Health Informatics (11 citations), Artificial Intelligence (196 citations), Safety, Risk, Reliability and Quality (31 citations) and Safety Research (27 citations). Rob Ashmore has collaborated with scholars based in United Kingdom. Frequent co-authors include Colin Paterson, Radu Călinescu, Matthew Q. Hill, Xiaowei Huang, James J. Sharp, Youcheng Sun, Daniel Kroening, Alec Banks, Rob Alexander and Hamid Asgari. Their work appears in journals such as ACM Transactions on Embedded Computing Systems, ACM Computing Surveys, Lecture notes in computer science, White Rose Research Online (University of Leeds, The University of Sheffield, University of York) and National Conference on Artificial Intelligence.

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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