Jonathan M. Garibaldi

10.2k total citations · 1 hit paper
274 papers, 6.5k citations indexed

About

Jonathan M. Garibaldi is a scholar working on Artificial Intelligence, Management Science and Operations Research and Molecular Biology. According to data from OpenAlex, Jonathan M. Garibaldi has authored 274 papers receiving a total of 6.5k indexed citations (citations by other indexed papers that have themselves been cited), including 146 papers in Artificial Intelligence, 48 papers in Management Science and Operations Research and 46 papers in Molecular Biology. Recurrent topics in Jonathan M. Garibaldi's work include Fuzzy Logic and Control Systems (84 papers), Neural Networks and Applications (45 papers) and Fuzzy Systems and Optimization (36 papers). Jonathan M. Garibaldi is often cited by papers focused on Fuzzy Logic and Control Systems (84 papers), Neural Networks and Applications (45 papers) and Fuzzy Systems and Optimization (36 papers). Jonathan M. Garibaldi collaborates with scholars based in United Kingdom, China and United States. Jonathan M. Garibaldi's co-authors include Robert John, Jenna Reps, Nadeem Qureshi, Joe Kai, Stephen Weng, Christian Wagner, P.R. Innocent, Daniele Soria, Heiko Hirschmüller and Ian O. Ellis and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Cancer Research.

In The Last Decade

Jonathan M. Garibaldi

270 papers receiving 6.3k citations

Hit Papers

align trajectories sort by overperformance

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.

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

2017 820 citations Jenna Reps, Jonathan M. Garibaldi et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Jonathan M. Garibaldi	2.4k	1.0k	930	808	565	274	6.5k
Mihaela van der Schaar	2.7k 1.1×	416 0.4×	926 1.0×	2.1k 2.6×	419 0.7×	611	12.9k
Ioannis Tsamardinos	2.8k 1.2×	1.7k 1.7×	561 0.6×	589 0.7×	305 0.5×	135	5.6k
Yoichi Hayashi	2.0k 0.8×	603 0.6×	489 0.5×	232 0.3×	452 0.8×	267	6.2k
Constantin Aliferis	2.8k 1.2×	2.0k 1.9×	546 0.6×	478 0.6×	305 0.5×	114	6.3k
Nan Liu	2.2k 0.9×	1.1k 1.0×	595 0.6×	435 0.5×	108 0.2×	577	10.0k
Shu‐Kay Ng	2.2k 0.9×	962 0.9×	176 0.2×	599 0.7×	349 0.6×	179	7.7k
Dan Geiger	5.6k 2.3×	2.1k 2.1×	1.0k 1.1×	620 0.8×	556 1.0×	97	10.5k
Igor Kononenko	3.7k 1.6×	855 0.8×	372 0.4×	1.1k 1.4×	169 0.3×	117	7.9k
Xiaoqian Jiang	3.7k 1.6×	1.2k 1.2×	213 0.2×	466 0.6×	260 0.5×	342	8.4k
Pedro Larrañaga	5.5k 2.3×	3.2k 3.1×	527 0.6×	1.6k 2.0×	332 0.6×	231	11.9k

Countries citing papers authored by Jonathan M. Garibaldi

Since Specialization

Citations

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

Fields of papers citing papers by Jonathan M. Garibaldi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan M. Garibaldi. 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 Jonathan M. Garibaldi. The network helps show where Jonathan M. Garibaldi may publish in the future.

Co-authorship network of co-authors of Jonathan M. Garibaldi

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

All Works

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20 of 20 papers shown

Xu, Qing, Jiaxuan Li, Xiangjian He, et al.. (2025). De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation. IEEE Transactions on Circuits and Systems for Video Technology. 36(3). 3782–3794. 1 indexed citations

Pacitti, Esther, Florent Masséglia, Reza Akbarinia, et al.. (2024). SoftED: Metrics for soft evaluation of time series event detection. Computers & Industrial Engineering. 198. 110728–110728. 3 indexed citations

Li, Jiawei, Tianxiang Cui, Huan Jin, et al.. (2024). A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem. Expert Systems with Applications. 249. 123515–123515. 3 indexed citations

Garibaldi, Jonathan M., et al.. (2023). Reshaping Wearable Robots Using Fuzzy Intelligence: Integrating Type-2 Fuzzy Decision, Intelligent Control, and Origami Structure. IEEE Transactions on Fuzzy Systems. 31(11). 3741–3761. 7 indexed citations

Qiao, Lin, Xin Chen, Chao Chen, & Jonathan M. Garibaldi. (2022). A Novel Quality Control Algorithm for Medical Image Segmentation Based on Fuzzy Uncertainty. IEEE Transactions on Fuzzy Systems. 31(8). 2532–2544. 14 indexed citations

Gander, Amir, et al.. (2022). Lessons learned from the COVID-19 pandemic about sample access for research in the UK. BMJ Open. 12(4). e047309–e047309. 4 indexed citations

Qiao, Lin, Xin Chen, Chao Chen, & Jonathan M. Garibaldi. (2022). Quality Quantification in Deep Convolutional Neural Networks for Skin Lesion Segmentation using Fuzzy Uncertainty Measurement. Repository@Nottingham (University of Nottingham). 1–8. 3 indexed citations

Xu, Bolei, Jingxin Liu, Xianxu Hou, et al.. (2019). Look, Investigate, and Classify: A Deep Hybrid Attention Method for Breast Cancer Classification. 914–918. 24 indexed citations

Figueredo, Grazziela P., Utkarsh Agrawal, Mohammad Mesgarpour, et al.. (2018). Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom. IEEE Transactions on Intelligent Transportation Systems. 20(9). 3324–3336. 23 indexed citations

10.

Todd, Ian, Ola H. Negm, Jenna Reps, et al.. (2017). A signalome screening approach in the autoinflammatory disease TNF receptor associated periodic syndrome (TRAPS) highlights the anti-inflammatory properties of drugs for repurposing. Pharmacological Research. 125(Pt B). 188–200. 8 indexed citations

11.

Garibaldi, Jonathan M., et al.. (2017). Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS. Digital Commons - Michigan Tech (Michigan Technological University). 1–6. 5 indexed citations

12.

Green, Andrew R., Daniele Soria, Des G. Powe, et al.. (2016). Nottingham prognostic index plus (NPI+) predicts risk of distant metastases in primary breast cancer. Breast Cancer Research and Treatment. 157(1). 65–75. 22 indexed citations

13.

Green, Andrew R., Des G. Powe, Emad A. Rakha, et al.. (2013). Identification of key clinical phenotypes of breast cancer using a reduced panel of protein biomarkers. British Journal of Cancer. 109(7). 1886–1894. 30 indexed citations

14.

Biganzoli, Elia, Danila Coradini, Federico Ambrogi, et al.. (2011). p53 Status Identifies Two Subgroups of Triple-negative Breast Cancers with Distinct Biological Features. Japanese Journal of Clinical Oncology. 41(2). 172–179. 48 indexed citations

15.

Glaab, Enrico, Jonathan M. Garibaldi, & Natalio Krasnogor. (2010). Learning pathway-based decision rules to classify microarray cancer samples. Open Repository and Bibliography (University of Luxembourg). 123–134. 5 indexed citations

16.

Elsheikh, Somaia, Andrew R. Green, Emad A. Rakha, et al.. (2009). Global Histone Modifications in Breast Cancer Correlate with Tumor Phenotypes, Prognostic Factors, and Patient Outcome. Cancer Research. 69(9). 3802–3809. 366 indexed citations

17.

Asmuni, Hishammuddin, Edmund Burke, & Jonathan M. Garibaldi. (2004). A Comparison of Fuzzy and Non-Fuzzy Ordering Heuristics for Examination Timetabling. 4 indexed citations

18.

Garibaldi, Jonathan M., et al.. (2004). The Application of a Simulated Annealing Fuzzy Clustering Algorithm for Cancer Diagnosis. 7 indexed citations

19.

Garibaldi, Jonathan M., et al.. (2003). Investigating Adaptation in Type-2 Fuzzy Logic Systems Applied to Umbilical Acid-Base Assessment. 31 indexed citations

20.

Garibaldi, Jonathan M., et al.. (1998). The Validation of a Fuzzy Expert System for Umbilical Cord Acid-Base Analysis. 3 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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