Jonathan P. Epperlein

661 total citations
29 papers, 431 citations indexed

About

Jonathan P. Epperlein is a scholar working on Control and Systems Engineering, Oncology and Surgery. According to data from OpenAlex, Jonathan P. Epperlein has authored 29 papers receiving a total of 431 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Control and Systems Engineering, 9 papers in Oncology and 7 papers in Surgery. Recurrent topics in Jonathan P. Epperlein's work include Colorectal Cancer Surgical Treatments (7 papers), Numerical methods for differential equations (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Jonathan P. Epperlein is often cited by papers focused on Colorectal Cancer Surgical Treatments (7 papers), Numerical methods for differential equations (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Jonathan P. Epperlein collaborates with scholars based in Ireland, United States and Netherlands. Jonathan P. Epperlein's co-authors include Julio Sáez-Rodríguez, Leonidas G. Alexopoulos, Steffen Klamt, Peter K. Sorger, Regina Samaga, Douglas A. Lauffenburger, Bassam Bamieh, Sergiy Zhuk, Ronan A. Cahill and Karl Johan Åström and has published in prestigious journals such as PLoS ONE, Automatica and British journal of surgery.

In The Last Decade

Jonathan P. Epperlein

28 papers receiving 427 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jonathan P. Epperlein Ireland 7 230 79 65 50 47 29 431
Yandong Miao China 14 319 1.4× 40 0.5× 144 2.2× 30 0.6× 41 0.9× 56 718
B. Mahr Austria 15 62 0.3× 67 0.8× 36 0.6× 48 1.0× 70 1.5× 36 553
Renato Guerrieri Italy 13 81 0.4× 13 0.2× 59 0.9× 71 1.4× 29 0.6× 31 381
Jinhyo Ahn South Korea 12 146 0.6× 43 0.5× 34 0.5× 158 3.2× 30 0.6× 18 601
Jianbing Wu China 13 280 1.2× 24 0.3× 112 1.7× 29 0.6× 45 1.0× 51 720
Lingzhi Hu China 12 195 0.8× 155 2.0× 13 0.2× 121 2.4× 25 0.5× 29 628
Subarna Sinha United States 15 233 1.0× 50 0.6× 70 1.1× 48 1.0× 12 0.3× 32 672
Jianqing Liang China 16 160 0.7× 18 0.2× 81 1.2× 43 0.9× 57 1.2× 40 626
Thibault Helleputte Belgium 8 328 1.4× 35 0.4× 29 0.4× 22 0.4× 45 1.0× 16 642

Countries citing papers authored by Jonathan P. Epperlein

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan P. Epperlein

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonathan P. Epperlein

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan P. Epperlein. A scholar is included among the top collaborators of Jonathan P. Epperlein 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 P. Epperlein. Jonathan P. Epperlein 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.
Hardy, Niall, Jeffrey Dalli, Jonathan P. Epperlein, et al.. (2025). Explainable endoscopic artificial intelligence method for real-time in situ significant rectal lesion characterization: a prospective cohort study. International Journal of Surgery. 111(2). 2313–2316. 2 indexed citations
2.
3.
Dalli, Jeffrey, et al.. (2024). Clinical and computational development of a patient-calibrated ICGFA bowel transection recommender. Surgical Endoscopy. 38(6). 3212–3222. 1 indexed citations
4.
Dalli, Jeffrey, et al.. (2024). Advancing indocyanine green fluorescence flap perfusion assessment via near infrared signal quantification. JPRAS Open. 41. 203–214. 1 indexed citations
5.
Hardy, Niall, Jeffrey Dalli, Jonathan P. Epperlein, et al.. (2024). Surgeon assessment of significant rectal polyps using white light endoscopy alone and in comparison to fluorescence-augmented AI lesion classification. Langenbeck s Archives of Surgery. 409(1). 170–170. 2 indexed citations
6.
Dalli, Jeffrey, Jonathan P. Epperlein, Niall Hardy, et al.. (2024). A feasibility study assessing quantitative indocyanine green angiographic predictors of reconstructive complications following nipple-sparing mastectomy. JPRAS Open. 40. 32–47. 3 indexed citations
7.
Hardy, Niall, Niall Mulligan, Jeffrey Dalli, et al.. (2024). Geotemporal Fluorophore Biodistribution Mapping of Colorectal Cancer: Micro and Macroscopic Insights. Current Oncology. 31(2). 849–861. 1 indexed citations
8.
Hardy, Niall, Jeffrey Dalli, Jonathan P. Epperlein, et al.. (2023). Clinical application of machine learning and computer vision to indocyanine green quantification for dynamic intraoperative tissue characterisation: how to do it. Surgical Endoscopy. 37(8). 6361–6370. 13 indexed citations
9.
Jagtap, Jaidip, Jonathan P. Epperlein, Anjishnu Banerjee, et al.. (2023). Dynamic NIR Fluorescence Imaging and Machine Learning Framework for Stratifying High vs. Low Notch-Dll4 Expressing Host Microenvironment in Triple-Negative Breast Cancer. Cancers. 15(5). 1460–1460. 2 indexed citations
10.
Dalli, Jeffrey, et al.. (2023). Evaluating clinical near-infrared surgical camera systems with a view to optimizing operator and computational signal analysis. Journal of Biomedical Optics. 28(3). 35002–35002. 9 indexed citations
11.
Dalli, Jeffrey, et al.. (2023). A Case Report Demonstrating Quantitative Indocyanine Green Fluorescence Angiography for Single- Versus Dual-vein Microvascular Anastomosis. Plastic & Reconstructive Surgery Global Open. 11(12). e5468–e5468. 1 indexed citations
12.
Hardy, Niall, Jonathan P. Epperlein, Jeffrey Dalli, et al.. (2023). Real-time administration of indocyanine green in combination with computer vision and artificial intelligence for the identification and delineation of colorectal liver metastases. Surgery Open Science. 12. 48–54. 9 indexed citations
13.
Epperlein, Jonathan P., Sergiy Zhuk, Pól Mac Aonghusa, et al.. (2021). Practical Perfusion Quantification in Multispectral Endoscopic Video: Using the Minutes after ICG Administration to Assess Tissue Pathology.. PubMed. 2021. 428–437. 4 indexed citations
14.
Epperlein, Jonathan P. & Bassam Bamieh. (2020). Frequency-domain Methods and Polynomial Optimization for Optimal Periodic Control of Linear Plants. IFAC-PapersOnLine. 53(2). 6736–6742. 1 indexed citations
15.
Epperlein, Jonathan P., Sergiy Zhuk, & Robert Shorten. (2019). Recovering Markov models from closed-loop data. Automatica. 103. 116–125. 5 indexed citations
16.
Epperlein, Jonathan P., et al.. (2018). Bayesian classifier for Route prediction with Markov chains. 5. 677–682. 12 indexed citations
17.
Epperlein, Jonathan P., Robert Shorten, & Sergiy Zhuk. (2017). Learning Markov Models from Closed Loop Data-sets. 2 indexed citations
18.
Epperlein, Jonathan P. & Bassam Bamieh. (2016). Spatially invariant embeddings of systems with boundaries. 6133–6139. 3 indexed citations
19.
Epperlein, Jonathan P.. (2014). Topics in Modeling and Control of Spatially Distributed Systems. eScholarship (California Digital Library). 4 indexed citations
20.
Epperlein, Jonathan P. & Bassam Bamieh. (2014). Distributed control of spatially invariant systems over Sobolev spaces. 2133–2138. 2 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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