Michael Mayo

48.4k total citations
55 papers, 676 citations indexed

About

Michael Mayo is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Aerospace Engineering. According to data from OpenAlex, Michael Mayo has authored 55 papers receiving a total of 676 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 13 papers in Computer Vision and Pattern Recognition and 7 papers in Aerospace Engineering. Recurrent topics in Michael Mayo's work include Wind Energy Research and Development (6 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Diabetes Management and Research (5 papers). Michael Mayo is often cited by papers focused on Wind Energy Research and Development (6 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Diabetes Management and Research (5 papers). Michael Mayo collaborates with scholars based in New Zealand, United States and United Kingdom. Michael Mayo's co-authors include Eibe Frank, Panos Patros, Stefan Krämer, Sarah Wakes, Ryan Paul, Bernhard Pfahringer, Lynne Chepulis, Thomas Zeng, Brad H. Nelson and Mauro Castellarin and has published in prestigious journals such as PLoS ONE, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Michael Mayo

52 papers receiving 648 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Mayo New Zealand 15 147 135 119 92 51 55 676
Guangsheng Chen China 19 125 0.9× 208 1.5× 165 1.4× 40 0.4× 24 0.5× 93 1.1k
Xiaoling Xia China 19 170 1.2× 289 2.1× 137 1.2× 120 1.3× 33 0.6× 84 1.2k
Zhicai Liu China 15 139 0.9× 155 1.1× 65 0.5× 58 0.6× 10 0.2× 72 737
Yumei Li China 18 94 0.6× 94 0.7× 53 0.4× 24 0.3× 24 0.5× 90 952
Johnny Park United States 17 89 0.6× 407 3.0× 265 2.2× 218 2.4× 39 0.8× 37 1.1k
Guoxing Yang China 16 153 1.0× 234 1.7× 79 0.7× 41 0.4× 55 1.1× 52 755
Qian Zheng China 20 81 0.6× 175 1.3× 53 0.4× 81 0.9× 144 2.8× 70 1.0k
Sungjin Ahn South Korea 17 519 3.5× 219 1.6× 217 1.8× 52 0.6× 52 1.0× 47 1.3k
Fei He China 17 92 0.6× 554 4.1× 89 0.7× 106 1.2× 117 2.3× 86 1.2k

Countries citing papers authored by Michael Mayo

Since Specialization
Citations

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

Fields of papers citing papers by Michael Mayo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Mayo

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Mayo. A scholar is included among the top collaborators of Michael Mayo 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 Michael Mayo. Michael Mayo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Mayo, Michael, et al.. (2025). Probabilistic Explanations for Entropic Knowledge Extraction for Automated Satellite Component Detection. Journal of Aerospace Information Systems. 22(4). 296–309. 1 indexed citations
2.
Mayo, Michael, et al.. (2024). A scalable convolutional neural network approach to fluid flow prediction in complex environments. Scientific Reports. 14(1). 23080–23080. 3 indexed citations
3.
Frank, Eibe, et al.. (2023). Feature extractor stacking for cross-domain few-shot learning. Machine Learning. 113(1). 121–158. 3 indexed citations
4.
Mayo, Michael, et al.. (2023). Predicting ophthalmic clinic non‐attendance using machine learning: Development and validation of models using nationwide data. Clinical and Experimental Ophthalmology. 51(8). 764–774. 6 indexed citations
5.
Wang, Hongyu, Henry Gouk, Eibe Frank, et al.. (2022). Experiments in cross‐domain few‐shot learning for image classification. Journal of the Royal Society of New Zealand. 53(1). 169–191. 3 indexed citations
6.
Patros, Panos, Melanie Po‐Leen Ooi, Michael Mayo, et al.. (2022). Rural AI: Serverless-Powered Federated Learning for Remote Applications. IEEE Internet Computing. 27(2). 28–34. 25 indexed citations
7.
Mayo, Michael, et al.. (2022). Surgical Tool Datasets for Machine Learning Research: A Survey. International Journal of Computer Vision. 130(9). 2222–2248. 16 indexed citations
8.
Mayo, Michael, et al.. (2022). Predicting glucose level with an adapted branch predictor. Computers in Biology and Medicine. 145. 105388–105388. 8 indexed citations
9.
Patros, Panos, Michael Mayo, Melanie Po‐Leen Ooi, et al.. (2022). Adaptive Edge-Cloud Environments for Rural AI. ORCA Online Research @Cardiff (Cardiff University). 74–83. 8 indexed citations
10.
Wakes, Sarah, Bernard O. Bauer, & Michael Mayo. (2021). A preliminary assessment of machine learning algorithms for predicting CFD‐simulated wind flow patterns over idealised foredunes. Journal of the Royal Society of New Zealand. 51(2). 290–306. 9 indexed citations
11.
Mayo, Michael, et al.. (2021). OctopusNet: Machine learning for intelligent management of surgical tools. Smart Health. 23. 100244–100244. 3 indexed citations
12.
Mayo, Michael & Eibe Frank. (2020). Improving Naive Bayes for Regression with Optimized Artificial Surrogate Data. Applied Artificial Intelligence. 34(6). 484–514. 9 indexed citations
13.
Mayo, Michael, et al.. (2019). A survey of neural network-based cancer prediction models from microarray data. Artificial Intelligence in Medicine. 97. 204–214. 84 indexed citations
14.
Mayo, Michael, Lynne Chepulis, & Ryan Paul. (2019). Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning. PLoS ONE. 14(12). e0225613–e0225613. 25 indexed citations
15.
Mayo, Michael, et al.. (2017). Enhancing Regulatory Compliance by Using Artificial Intelligence Text Mining to Identify Penalty Clauses in Legislation. Research Commons (University of Waikato). 7 indexed citations
16.
Perrone, John A., et al.. (2014). Proceedings of the 29th International Conference on Image and Vision Computing New Zealand. 1 indexed citations
17.
Linning, Rob, John P. Fellers, Matthew Dickinson, et al.. (2011). Gene discovery in EST sequences from the wheat leaf rust fungus Puccinia triticina sexual spores, asexual spores and haustoria, compared to other rust and corn smut fungi. BMC Genomics. 12(1). 161–161. 38 indexed citations
18.
Beretta, Lorenzo, et al.. (2010). A 3-factor epistatic model predicts digital ulcers in Italian scleroderma patients. European Journal of Internal Medicine. 21(4). 347–353. 6 indexed citations
19.
Mayo, Michael. (2005). Learning Petri net models of non-linear gene interactions. Biosystems. 82(1). 74–82. 15 indexed citations
20.
Mayo, Michael. (2004). A multi-player educational game for story writing. BMC Immunology. 22(1). 37–39. 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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